Merge remote-tracking branch 'origin/main' into il_tool

Signed-off-by: Lu Fang <fanglu@fb.com>
adjust config type and remove config path for simplicity
2025-09-08 17:33:55 -07:00 · 2025-09-08 17:23:15 -07:00 · 2025-09-08 16:50:34 -07:00 · 2025-09-08 15:28:03 -07:00 · 2025-09-08 22:05:26 +00:00 · 2025-09-08 20:44:13 +00:00
1390 changed files with 105460 additions and 49312 deletions
--- a/.buildkite/check-wheel-size.py
+++ b/.buildkite/check-wheel-size.py
@ -5,11 +5,11 @@ import os
 import sys
 import zipfile

-# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 400 MiB
-# Note that we have 400 MiB quota, please use it wisely.
-# See https://github.com/pypi/support/issues/3792 .
+# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 450 MiB
+# Note that we have 800 MiB quota, please use it wisely.
+# See https://github.com/pypi/support/issues/6326 .
 # Please also sync the value with the one in Dockerfile.
-VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 400))
+VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 450))


 def print_top_10_largest_files(zip_file):
--- a/.buildkite/generate_index.py
+++ b/.buildkite/generate_index.py
@ -8,7 +8,8 @@ template = """<!DOCTYPE html>
 <html>
    <body>
    <h1>Links for vLLM</h1/>
-        <a href="../{wheel_html_escaped}">{wheel}</a><br/>
+        <a href="../{x86_wheel_html_escaped}">{x86_wheel}</a><br/>
+        <a href="../{arm_wheel_html_escaped}">{arm_wheel}</a><br/>
    </body>
 </html>
 """
@ -21,7 +22,25 @@ filename = os.path.basename(args.wheel)

 with open("index.html", "w") as f:
    print(f"Generated index.html for {args.wheel}")
+    # sync the abi tag with .buildkite/scripts/upload-wheels.sh
+    if "x86_64" in filename:
+        x86_wheel = filename
+        arm_wheel = filename.replace("x86_64", "aarch64").replace(
+            "manylinux1", "manylinux2014"
+        )
+    elif "aarch64" in filename:
+        x86_wheel = filename.replace("aarch64", "x86_64").replace(
+            "manylinux2014", "manylinux1"
+        )
+        arm_wheel = filename
+    else:
+        raise ValueError(f"Unsupported wheel: {filename}")
    # cloudfront requires escaping the '+' character
    f.write(
-        template.format(wheel=filename, wheel_html_escaped=filename.replace("+", "%2B"))
+        template.format(
+            x86_wheel=x86_wheel,
+            x86_wheel_html_escaped=x86_wheel.replace("+", "%2B"),
+            arm_wheel=arm_wheel,
+            arm_wheel_html_escaped=arm_wheel.replace("+", "%2B"),
+        )
    )
--- a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml
+++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml
@ -1,12 +0,0 @@
-# For vllm script, with -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
-model_name: "HandH1998/QQQ-Llama-3-8b-g128"
-tasks:
- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.419
-  - name: "exact_match,flexible-extract"
-    value: 0.416
-limit: 1000
-num_fewshot: 5
--- a/.buildkite/lm-eval-harness/configs/models-large.txt
+++ b/.buildkite/lm-eval-harness/configs/models-large.txt
@ -3,4 +3,3 @@ Meta-Llama-3-70B-Instruct.yaml
 Mixtral-8x7B-Instruct-v0.1.yaml
 Qwen2-57B-A14-Instruct.yaml
 DeepSeek-V2-Lite-Chat.yaml
-Meta-Llama-3-8B-QQQ.yaml
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.4
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]

 usage() {
    echo``
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.4
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]

 usage() {
    echo``
--- a/.buildkite/nightly-benchmarks/README.md
+++ b/.buildkite/nightly-benchmarks/README.md
@ -7,7 +7,7 @@ This directory contains two sets of benchmark for vllm.
 - Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
 - Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.

-See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.

 ## Performance benchmark quick overview

@ -138,28 +138,20 @@ The raw benchmarking results (in the format of json files) are in the `Artifacts

 The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
 When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
+`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
+If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.

-Here is an example using the script to compare result_a and result_b without detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json --ignore_test_name`
-
-|    | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|----------------------------------------|----------------------------------------|----------|
-| 0  | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | 241.620334                             | 294.018783                             | 1.216863 |
-| 2  | 218.298905                             | 262.664916                             | 1.203235 |
-| 3  | 242.743860                             | 299.816190                             | 1.235113 |
-
-Here is an example using the script to compare result_a and result_b with detail test name.
+Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
 `python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`

-|   | results_a/benchmark_results.json_name | results_a/benchmark_results.json | results_b/benchmark_results.json_name | results_b/benchmark_results.json | perf_ratio        |
-|---|---------------------------------------------|----------------------------------------|---------------------------------------------|----------------------------------------|----------|
-| 0 | serving_llama8B_tp1_sharegpt_qps_1          | 142.633982                             | serving_llama8B_tp1_sharegpt_qps_1          | 156.526018                             | 1.097396 |
-| 1 | serving_llama8B_tp1_sharegpt_qps_16         | 241.620334                             | serving_llama8B_tp1_sharegpt_qps_16         | 294.018783                             | 1.216863 |
-| 2 | serving_llama8B_tp1_sharegpt_qps_4          | 218.298905                             | serving_llama8B_tp1_sharegpt_qps_4          | 262.664916                             | 1.203235 |
-| 3 | serving_llama8B_tp1_sharegpt_qps_inf        | 242.743860                             | serving_llama8B_tp1_sharegpt_qps_inf        | 299.816190                             | 1.235113 |
-| 4 | serving_llama8B_tp2_random_1024_128_qps_1   | 96.613390                              | serving_llama8B_tp4_random_1024_128_qps_1   | 108.404853                             | 1.122048 |
+|   | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
+|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
+| 0  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982                             | 156.526018                             | 1.097396 |
+| 1  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334                             | 294.018783                             | 1.216863 |
+
+A comparison diagram will be generated below the table.
+Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
+<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />

 ## Nightly test details

@ -168,9 +160,9 @@ See [nightly-descriptions.md](nightly-descriptions.md) for the detailed descript
 ### Workflow

 - The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
+- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
+- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.

 ### Nightly tests

@ -180,6 +172,6 @@ In [nightly-tests.json](tests/nightly-tests.json), we include the command line a

 The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.

-WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.

 WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
--- a/.buildkite/nightly-benchmarks/nightly-descriptions.md
+++ b/.buildkite/nightly-benchmarks/nightly-descriptions.md
@ -17,7 +17,7 @@ Latest reproduction guilde: [github issue link](https://github.com/vllm-project/
    - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
    - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
    - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
-        - *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
+        - *NOTE: we use r24.07 as the current implementation only works for this version. We are going to bump this up.*
    - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
 - Hardware
    - 8x Nvidia A100 GPUs
--- a/.buildkite/nightly-benchmarks/scripts/compare-json-results.py
+++ b/.buildkite/nightly-benchmarks/scripts/compare-json-results.py
@ -1,33 +1,202 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import argparse
+import json
+import os
+from importlib import util

 import pandas as pd

+plotly_found = util.find_spec("plotly.express") is not None
+

 def compare_data_columns(
-    files, name_column, data_column, drop_column, ignore_test_name=False
+    files, name_column, data_column, info_cols, drop_column, debug=False
 ):
-    print("\ncompare_data_column: " + data_column)
-    frames = []
-    compare_frames = []
-    for file in files:
-        data_df = pd.read_json(file)
-        serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
-        if ignore_test_name is False:
-            serving_df = serving_df.rename(columns={name_column: file + "_name"})
-            frames.append(serving_df[file + "_name"])
-        serving_df = serving_df.rename(columns={data_column: file})
-        frames.append(serving_df[file])
-        compare_frames.append(serving_df[file])
-        if len(compare_frames) >= 2:
-            # Compare numbers among two files
-            ratio_df = compare_frames[1] / compare_frames[0]
-            frames.append(ratio_df)
-            compare_frames.pop(1)
+    """
+    Align concatenation by keys derived from info_cols instead of row order.
+    - Pick one canonical key list: subset of info_cols present in ALL files.
+    - For each file: set index to those keys, aggregate duplicates
+    - (mean for metric, first for names).
+    - Concat along axis=1 (indexes align), then reset_index so callers can
+    - group by columns.
+    - If --debug, add a <file_label>_name column per file.
+    """
+    print("\ncompare_data_column:", data_column)

+    frames = []
+    raw_data_cols = []
+    compare_frames = []
+
+    # 1) choose a canonical key list from info_cols that exists in ALL files
+    cols_per_file = []
+    for f in files:
+        try:
+            df_tmp = pd.read_json(f, orient="records")
+        except Exception as err:
+            raise ValueError(f"Failed to read {f}") from err
+        cols_per_file.append(set(df_tmp.columns))
+
+    key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
+    if not key_cols:
+        # soft fallback: use any info_cols present in the first file
+        key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
+    if not key_cols:
+        raise ValueError(
+            "No common key columns found from info_cols across the input files."
+        )
+
+    # 2) build a single "meta" block (keys as columns) once, aligned by the key index
+    meta_added = False
+
+    for file in files:
+        df = pd.read_json(file, orient="records")
+
+        # Keep rows that actually have the compared metric (same as original behavior)
+        if drop_column in df.columns:
+            df = df.dropna(subset=[drop_column], ignore_index=True)
+
+        # Stabilize numeric key columns (harmless if missing)
+        for c in (
+            "Input Len",
+            "Output Len",
+            "TP Size",
+            "PP Size",
+            "# of max concurrency.",
+            "qps",
+        ):
+            if c in df.columns:
+                df[c] = pd.to_numeric(df[c], errors="coerce")
+
+        # Ensure all key columns exist
+        for c in key_cols:
+            if c not in df.columns:
+                df[c] = pd.NA
+
+        # Set index = key_cols and aggregate duplicates → unique MultiIndex
+        df_idx = df.set_index(key_cols, drop=False)
+
+        # meta (key columns), unique per key
+        meta = df_idx[key_cols]
+        if not meta.index.is_unique:
+            meta = meta.groupby(level=key_cols, dropna=False).first()
+
+        # metric series for this file, aggregated to one row per key
+        file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
+        s = df_idx[data_column]
+        if not s.index.is_unique:
+            s = s.groupby(level=key_cols, dropna=False).mean()
+        s.name = file_label  # column label like original
+
+        # add meta once (from first file) so keys are the leftmost columns
+        if not meta_added:
+            frames.append(meta)
+            meta_added = True
+
+        # (NEW) debug: aligned test-name column per file
+        if debug and name_column in df_idx.columns:
+            name_s = df_idx[name_column]
+            if not name_s.index.is_unique:
+                name_s = name_s.groupby(level=key_cols, dropna=False).first()
+            name_s.name = f"{file_label}_name"
+            frames.append(name_s)
+
+        frames.append(s)
+        raw_data_cols.append(file_label)
+        compare_frames.append(s)
+
+        # Generalize ratio: for any file N>=2, add ratio (fileN / file1)
+        if len(compare_frames) >= 2:
+            base = compare_frames[0]
+            current = compare_frames[-1]
+            ratio = current / base
+            ratio = ratio.mask(base == 0)  # avoid inf when baseline is 0
+            ratio.name = f"Ratio 1 vs {len(compare_frames)}"
+            frames.append(ratio)
+
+    # 4) concat on columns with aligned MultiIndex;
+    # then reset_index to return keys as columns
    concat_df = pd.concat(frames, axis=1)
-    return concat_df
+    concat_df = concat_df.reset_index(drop=True).reset_index()
+    if "index" in concat_df.columns:
+        concat_df = concat_df.drop(columns=["index"])
+
+    # Ensure key/info columns appear first (in your info_cols order)
+    front = [c for c in info_cols if c in concat_df.columns]
+    rest = [c for c in concat_df.columns if c not in front]
+    concat_df = concat_df[front + rest]
+
+    print(raw_data_cols)
+    return concat_df, raw_data_cols
+
+
+def split_json_by_tp_pp(
+    input_file: str = "benchmark_results.json", output_root: str = "."
+) -> list[str]:
+    """
+    Split a benchmark JSON into separate folders by (TP Size, PP Size).
+
+    Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
+    Returns: list of file paths written.
+    """
+    # Load JSON data into DataFrame
+    with open(input_file, encoding="utf-8") as f:
+        data = json.load(f)
+
+    # If the JSON is a dict with a list under common keys, use that list
+    if isinstance(data, dict):
+        for key in ("results", "serving_results", "benchmarks", "data"):
+            if isinstance(data.get(key), list):
+                data = data[key]
+                break
+
+    df = pd.DataFrame(data)
+
+    # Keep only "serving" tests
+    name_col = next(
+        (c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
+    )
+    if name_col:
+        df = df[
+            df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
+        ].copy()
+
+    # Handle alias column names
+    rename_map = {
+        "tp_size": "TP Size",
+        "tensor_parallel_size": "TP Size",
+        "pp_size": "PP Size",
+        "pipeline_parallel_size": "PP Size",
+    }
+    df.rename(
+        columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
+    )
+
+    # Ensure TP/PP columns exist (default to 1 if missing)
+    if "TP Size" not in df.columns:
+        df["TP Size"] = 1
+    if "PP Size" not in df.columns:
+        df["PP Size"] = 1
+
+    # make sure TP/PP are numeric ints with no NaN
+    df["TP Size"] = (
+        pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
+    )
+    df["PP Size"] = (
+        pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
+    )
+
+    # Split into separate folders
+    saved_paths: list[str] = []
+    for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
+        folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
+        os.makedirs(folder_name, exist_ok=True)
+        filepath = os.path.join(folder_name, "benchmark_results.json")
+        group_df.to_json(filepath, orient="records", indent=2, force_ascii=False)
+        print(f"Saved: {filepath}")
+        saved_paths.append(filepath)
+
+    return saved_paths


 if __name__ == "__main__":
@ -36,31 +205,103 @@ if __name__ == "__main__":
        "-f", "--file", action="append", type=str, help="input file name"
    )
    parser.add_argument(
-        "--ignore_test_name", action="store_true", help="ignore_test_name or not"
+        "--debug", action="store_true", help="show all information for debugging"
+    )
+    parser.add_argument(
+        "--plot",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="plot perf diagrams or not --no-plot --plot",
+    )
+    parser.add_argument(
+        "-x",
+        "--xaxis",
+        type=str,
+        default="# of max concurrency.",
+        help="column name to use as X Axis in comparison graph",
    )
    args = parser.parse_args()
-    files = args.file
-    print("comparing : " + ", ".join(files))

    drop_column = "P99"
    name_column = "Test name"
+    info_cols = [
+        "Model",
+        "Dataset Name",
+        "Input Len",
+        "Output Len",
+        "TP Size",
+        "PP Size",
+        "# of max concurrency.",
+        "qps",
+    ]
    data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
    html_msgs_for_data_cols = [
        "Compare Output Tokens /n",
        "Median TTFT /n",
        "Median TPOT /n",
    ]
-    ignore_test_name = args.ignore_test_name
+
+    if len(args.file) == 1:
+        files = split_json_by_tp_pp(args.file[0], output_root="splits")
+        info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
+    else:
+        files = args.file
+    print("comparing : " + ", ".join(files))
+    debug = args.debug
+    plot = args.plot
+    # For Plot feature, assign y axis from one of info_cols
+    y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
    with open("perf_comparison.html", "w") as text_file:
        for i in range(len(data_cols_to_compare)):
-            output_df = compare_data_columns(
+            output_df, raw_data_cols = compare_data_columns(
                files,
                name_column,
                data_cols_to_compare[i],
+                info_cols,
                drop_column,
-                ignore_test_name=ignore_test_name,
+                debug=debug,
            )
-            print(output_df)
-            html = output_df.to_html()
-            text_file.write(html_msgs_for_data_cols[i])
-            text_file.write(html)
+
+            # For Plot feature, insert y axis from one of info_cols
+            raw_data_cols.insert(0, info_cols[y_axis_index])
+
+            filtered_info_cols = info_cols[:-2]
+            existing_group_cols = [
+                c for c in filtered_info_cols if c in output_df.columns
+            ]
+            if not existing_group_cols:
+                raise ValueError(
+                    f"No valid group-by columns  "
+                    f"Expected subset: {filtered_info_cols}, "
+                    f"but DataFrame has: {list(output_df.columns)}"
+                )
+            output_df_sorted = output_df.sort_values(by=existing_group_cols)
+            output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
+            for name, group in output_groups:
+                html = group.to_html()
+                text_file.write(html_msgs_for_data_cols[i])
+                text_file.write(html)
+
+                if plot and plotly_found:
+                    import plotly.express as px
+
+                    df = group[raw_data_cols]
+                    df_sorted = df.sort_values(by=info_cols[y_axis_index])
+                    # Melt DataFrame for plotting
+                    df_melted = df_sorted.melt(
+                        id_vars=info_cols[y_axis_index],
+                        var_name="Configuration",
+                        value_name=data_cols_to_compare[i],
+                    )
+                    title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
+                    # Create Plotly line chart
+                    fig = px.line(
+                        df_melted,
+                        x=info_cols[y_axis_index],
+                        y=data_cols_to_compare[i],
+                        color="Configuration",
+                        title=title,
+                        markers=True,
+                    )
+                    # Export to HTML
+                    text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))
--- a/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
+++ b/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
@ -1,17 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+import argparse
 import json
 import os
+import shlex
 from importlib import util
 from pathlib import Path
+from typing import Any

 import pandas as pd
 import psutil
+import regex as re
 from tabulate import tabulate

-results_folder = Path("results/")
-
 # latency results and the keys that will be printed into markdown
 latency_results = []
 latency_column_mapping = {
@ -42,14 +44,22 @@ throughput_results_column_mapping = {
 serving_results = []
 serving_column_mapping = {
    "test_name": "Test name",
+    "model_id": "Model",
+    "dataset_name": "Dataset Name",
+    "input_len": "Input Len",
+    "output_len": "Output Len",
+    "tp_size": "TP Size",
+    "pp_size": "PP Size",
+    "dtype": "dtype",
    "gpu_type": "GPU",
    "completed": "# of req.",
+    "qps": "qps",
    "max_concurrency": "# of max concurrency.",
    "request_throughput": "Tput (req/s)",
    "total_token_throughput": "Total Token Tput (tok/s)",
    "output_throughput": "Output Tput (tok/s)",
-    "total_input_tokens": "Total input tokens",
-    "total_output_tokens": "Total output tokens",
+    # "total_input_tokens": "Total input tokens",
+    # "total_output_tokens": "Total output tokens",
    "mean_ttft_ms": "Mean TTFT (ms)",
    "median_ttft_ms": "Median TTFT (ms)",
    "p99_ttft_ms": "P99 TTFT (ms)",
@ -94,7 +104,104 @@ def get_size_with_unit(bytes, suffix="B"):
        bytes /= factor


+def _coerce(val: str) -> Any:
+    """Best-effort type coercion from string to Python types."""
+    low = val.lower()
+    if low == "null":
+        return None
+    if low == "true":
+        return True
+    if low == "false":
+        return False
+    # integers
+    if re.fullmatch(r"[+-]?\d+", val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+    # floats (keep 'inf'/'-inf'/'nan' as strings)
+    if re.fullmatch(r"[+-]?\d*\.\d+", val):
+        try:
+            return float(val)
+        except ValueError:
+            pass
+    return val
+
+
+def parse_client_command(cmd: str) -> dict[str, Any]:
+    """Parse the client_command shell string into {executable, script, args}."""
+    toks = shlex.split(cmd)
+    if len(toks) < 2:
+        raise ValueError("client_command must include an executable and a script")
+    executable, script = toks[0], toks[1]
+    args: dict[str, Any] = {}
+
+    i = 2
+    while i < len(toks):
+        t = toks[i]
+        if t.startswith("--"):
+            # --key=value or --key (value) or boolean flag
+            if "=" in t:
+                key, val = t.split("=", 1)
+                if key == "--metadata":
+                    md = {}
+                    if val:
+                        if "=" in val:
+                            k, v = val.split("=", 1)
+                            md[k] = _coerce(v)
+                        else:
+                            md[val] = True
+                    args[key] = md
+                else:
+                    args[key] = _coerce(val)
+                i += 1
+                continue
+
+            key = t
+
+            # Special: consume metadata k=v pairs until next --flag
+            if key == "--metadata":
+                i += 1
+                md = {}
+                while i < len(toks) and not toks[i].startswith("--"):
+                    pair = toks[i]
+                    if "=" in pair:
+                        k, v = pair.split("=", 1)
+                        md[k] = _coerce(v)
+                    else:
+                        md[pair] = True
+                    i += 1
+                args[key] = md
+                continue
+
+            # Standard: check if next token is a value (not a flag)
+            if i + 1 < len(toks) and not toks[i + 1].startswith("--"):
+                args[key] = _coerce(toks[i + 1])
+                i += 2
+            else:
+                # lone flag -> True
+                args[key] = True
+                i += 1
+        else:
+            # unexpected positional; skip
+            i += 1
+
+    return {"executable": executable, "script": script, "args": args}
+
+
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--result",
+        type=str,
+        default="results",
+        help="Folder name for benchmark output results.",
+    )
+    args = parser.parse_args()
+    results_folder = Path(args.result)
+    if not results_folder.exists():
+        raise FileNotFoundError(f"results folder does not exist: {results_folder}")
    # collect results
    for test_file in results_folder.glob("*.json"):
        with open(test_file) as f:
@ -102,7 +209,6 @@ if __name__ == "__main__":

        if "serving" in str(test_file):
            # this result is generated via `vllm bench serve` command
-
            # attach the benchmarking command to raw_result
            try:
                with open(test_file.with_suffix(".commands")) as f:
@ -110,12 +216,44 @@ if __name__ == "__main__":
            except OSError as e:
                print(e)
                continue
+            # Parse Server Command Arg
+            out: dict[str, Any] = {
+                "server_command": parse_client_command(command["server_command"])
+            }
+            parse_args = [
+                "--tensor-parallel-size",
+                "--pipeline-parallel-size",
+                "--dtype",
+            ]
+            col_mapping = ["tp_size", "pp_size", "dtype"]
+            for index, arg in enumerate(parse_args):
+                if arg in out["server_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["server_command"]["args"][arg]}
+                    )

+            # Parse Client Command Arg
+            out: dict[str, Any] = {
+                "client_command": parse_client_command(command["client_command"])
+            }
+            parse_args = [
+                "--dataset-name",
+                "--random-input-len",
+                "--random-output-len",
+                "--request-rate",
+            ]
+            col_mapping = ["dataset_name", "input_len", "output_len", "qps"]
+
+            for index, arg in enumerate(parse_args):
+                if arg in out["client_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["client_command"]["args"][arg]}
+                    )
+            # Add Server, Client command
            raw_result.update(command)

            # update the test name of this result
            raw_result.update({"test_name": test_file.stem})
-
            # add the result to raw_result
            serving_results.append(raw_result)
            continue
@ -205,7 +343,10 @@ if __name__ == "__main__":
            columns=latency_column_mapping
        )
    if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+        valid_columns = [
+            col for col in serving_column_mapping if col in serving_results.columns
+        ]
+        serving_results = serving_results[valid_columns].rename(
            columns=serving_column_mapping
        )
    if not throughput_results.empty:
@ -245,7 +386,9 @@ if __name__ == "__main__":
    )

    # document the result
-    with open(results_folder / "benchmark_results.md", "w") as f:
+    md_file = "benchmark_results.md"
+    json_file = "benchmark_results.json"
+    with open(results_folder / md_file, "w") as f:
        results = read_markdown(
            "../.buildkite/nightly-benchmarks/"
            + "performance-benchmarks-descriptions.md"
@ -260,7 +403,7 @@ if __name__ == "__main__":
        f.write(results)

    # document benchmarking results in json
-    with open(results_folder / "benchmark_results.json", "w") as f:
+    with open(results_folder / json_file, "w") as f:
        results = (
            latency_results.to_dict(orient="records")
            + throughput_results.to_dict(orient="records")
--- a/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
@ -382,7 +382,7 @@ run_genai_perf_tests() {
      client_command="genai-perf profile \
        -m $model \
        --service-kind openai \
-        --backend vllm \
+        --backend "$backend" \
        --endpoint-type chat \
        --streaming \
        --url localhost:$port \
--- a/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
@ -194,9 +194,11 @@ run_latency_tests() {

    # check if there is enough GPU to run the test
    tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
        continue
      fi
    else
@ -261,9 +263,11 @@ run_throughput_tests() {

    # check if there is enough GPU to run the test
    tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
        continue
      fi
    else
@ -329,12 +333,21 @@ run_serving_tests() {
    qps_list=$(echo "$params" | jq -r '.qps_list')
    qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
    echo "Running over qps list $qps_list"
+    max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
+    if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
+        num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
+        max_concurrency_list="[$num_prompts]"
+    fi
+    max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
+    echo "Running over max concurrency list $max_concurrency_list"

    # check if there is enough resources to run the test
    tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+    if [ "$ON_CPU" == "1" ]; then
+      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
        continue
      fi
    else
@ -390,35 +403,39 @@ run_serving_tests() {
        echo "now qps is $qps"
      fi

-      new_test_name=$test_name"_qps_"$qps
+      # iterate over different max_concurrency
+      for max_concurrency in $max_concurrency_list; do
+        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
+        echo " new test name $new_test_name"
+        # pass the tensor parallel size to the client so that it can be displayed
+        # on the benchmark dashboard
+        client_command="vllm bench serve \
+          --save-result \
+          --result-dir $RESULTS_FOLDER \
+          --result-filename ${new_test_name}.json \
+          --request-rate $qps \
+          --max-concurrency $max_concurrency \
+          --metadata "tensor_parallel_size=$tp" \
+          $client_args $client_remote_args "

-      # pass the tensor parallel size to the client so that it can be displayed
-      # on the benchmark dashboard
-      client_command="vllm bench serve \
-        --save-result \
-        --result-dir $RESULTS_FOLDER \
-        --result-filename ${new_test_name}.json \
-        --request-rate $qps \
-        --metadata "tensor_parallel_size=$tp" \
-        $client_args $client_remote_args "
+        echo "Running test case $test_name with qps $qps"
+        echo "Client command: $client_command"

-      echo "Running test case $test_name with qps $qps"
-      echo "Client command: $client_command"
+        bash -c "$client_command"

-      bash -c "$client_command"
-
-      # record the benchmarking commands
-      jq_output=$(jq -n \
-        --arg server "$server_command" \
-        --arg client "$client_command" \
-        --arg gpu "$gpu_type" \
-        '{
-          server_command: $server,
-          client_command: $client,
-          gpu_type: $gpu
-        }')
-      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
+        # record the benchmarking commands
+        jq_output=$(jq -n \
+          --arg server "$server_command" \
+          --arg client "$client_command" \
+          --arg gpu "$gpu_type" \
+          '{
+            server_command: $server,
+            client_command: $client,
+            gpu_type: $gpu
+          }')
+        echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"

+      done
    done

    # clean up
--- a/.buildkite/nightly-benchmarks/tests/genai-perf-tests.json
+++ b/.buildkite/nightly-benchmarks/tests/genai-perf-tests.json
@ -12,7 +12,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
--- a/.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json
+++ b/.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json
@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
            "load_format": "dummy",
            "num_iters_warmup": 5,
@ -20,7 +20,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
            "load_format": "dummy",
            "num_iters_warmup": 5,
--- a/.buildkite/nightly-benchmarks/tests/nightly-tests.json
+++ b/.buildkite/nightly-benchmarks/tests/nightly-tests.json
@ -36,7 +36,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
@ -90,7 +89,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
@ -144,7 +142,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
@ -195,7 +192,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
@ -248,7 +244,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
@ -301,7 +296,6 @@
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
--- a/.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc2.json
+++ b/.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc2.json
@ -1,7 +1,8 @@
 [
    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -23,17 +24,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -55,17 +56,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -87,17 +88,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_tp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -120,19 +121,19 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    },
    {
-        "test_name": "serving_llama8B_tp2_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -155,19 +156,19 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    },
    {
-        "test_name": "serving_llama8B_tp4_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -176,7 +177,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -190,13 +191,419 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp4_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp4_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    }
--- a/.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json
+++ b/.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json
@ -1,7 +1,8 @@
 [
    {
-        "test_name": "serving_llama8B_pp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_pp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -23,17 +24,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -42,7 +43,39 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -55,17 +88,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_tp2pp6_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -74,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 2,
            "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@ -88,17 +121,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
-        "test_name": "serving_llama8B_pp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_pp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -107,7 +140,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -121,28 +154,63 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    },
    {
-        "test_name": "serving_llama8B_pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL:": 1,
+	    "VLLM_CPU_SGL_KERNEL": 1,
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_bf16_pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -156,19 +224,19 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    },
    {
-        "test_name": "serving_llama8B_tp2pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "test_name": "serving_llama8B_bf16_tp2pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -177,7 +245,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 2,
            "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@ -192,13 +260,560 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_pp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "pipeline_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_pp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "pipeline_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int8_tp2pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_pp1_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "pipeline_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2pp3_sharegpt",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_pp1_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "pipeline_parallel_size": 1,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+            "backend": "vllm",
+            "dataset_name": "random",
+	    "random-input-len": 128,
+	    "random-output-len": 128,
+	    "ignore-eos": "",
+            "num_prompts": 1000
+        }
+    },
+    {
+        "test_name": "serving_llama8B_int4_tp2pp3_random_128_128",
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+	    "quantization": "awq",
+            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 3,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+	    "enable_chunked_prefill": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
            "num_prompts": 1000
        }
    }
--- a/.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json
+++ b/.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json
@ -2,6 +2,7 @@
    {
        "test_name": "serving_llama8B_tp1_sharegpt",
        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -23,17 +24,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
        "test_name": "serving_llama8B_tp2_sharegpt",
        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -55,17 +56,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
        "test_name": "serving_llama8B_tp4_sharegpt",
        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -87,17 +88,17 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
            "num_prompts": 200
        }
    },
    {
        "test_name": "serving_llama8B_tp4_random_1024_128",
        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -120,19 +121,19 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
            "num_prompts": 100
        }
    },
    {
        "test_name": "serving_llama8B_pp6_random_1024_128",
        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
        "server_environment_variables": {
            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "pipeline_parallel_size": 6,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@ -155,13 +156,12 @@
            "load_format": "dummy"
        },
        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "backend": "vllm",
            "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
            "num_prompts": 100
        }
    }
--- a/.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json
+++ b/.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json
@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
            "load_format": "dummy",
            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
@ -21,7 +21,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
        },
        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 4,
            "load_format": "dummy",
            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
--- a/.buildkite/release-pipeline.yaml
+++ b/.buildkite/release-pipeline.yaml
@ -1,5 +1,24 @@
 steps:
+  # aarch64 + CUDA builds. PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
+  - label: "Build arm64 wheel - CUDA 12.9"
+    id: build-wheel-arm64-cuda-12-9
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
+      # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "mkdir artifacts"
+      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
+      - "bash .buildkite/scripts/upload-wheels.sh"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  - block: "Build CUDA 12.8 wheel"
+    key: block-build-cu128-wheel
+
  - label: "Build wheel - CUDA 12.8"
+    depends_on: block-build-cu128-wheel
    id: build-wheel-cuda-12-8
    agents:
      queue: cpu_queue_postmerge
@ -11,7 +30,12 @@ steps:
    env:
      DOCKER_BUILDKIT: "1"

+  - block: "Build CUDA 12.6 wheel"
+    key: block-build-cu126-wheel
+    depends_on: ~
+
  - label: "Build wheel - CUDA 12.6"
+    depends_on: block-build-cu126-wheel
    id: build-wheel-cuda-12-6
    agents:
      queue: cpu_queue_postmerge
@ -23,44 +47,63 @@ steps:
    env:
      DOCKER_BUILDKIT: "1"

-  # Note(simon): We can always build CUDA 11.8 wheel to ensure the build is working.
-  # However, this block can be uncommented to save some compute hours.
-  # - block: "Build CUDA 11.8 wheel"
-  #   key: block-build-cu118-wheel
-
-  - label: "Build wheel - CUDA 11.8"
-    # depends_on: block-build-cu118-wheel
-    id: build-wheel-cuda-11-8
+  # x86 + CUDA builds
+  - label: "Build wheel - CUDA 12.9"
+    depends_on: ~
+    id: build-wheel-cuda-12-9
    agents:
      queue: cpu_queue_postmerge
    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
      - "mkdir artifacts"
      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
      - "bash .buildkite/scripts/upload-wheels.sh"
    env:
      DOCKER_BUILDKIT: "1"

-  - block: "Build release image"
+  - label: "Build release image (x86)"
    depends_on: ~
-    key: block-release-image-build
-
-  - label: "Build release image"
-    depends_on: block-release-image-build
-    id: build-release-image
+    id: build-release-image-x86
    agents:
      queue: cpu_queue_postmerge
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+      # re-tag to default image tag and push, just in case arm64 build fails
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"

+  # PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
+  - label: "Build release image (arm64)"
+    depends_on: ~
+    id: build-release-image-arm64
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+
+  # Add job to create multi-arch manifest
+  - label: "Create multi-arch manifest"
+    depends_on:
+      - build-release-image-x86
+      - build-release-image-arm64
+    id: create-multi-arch-manifest
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
+      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+
  - label: "Annotate release workflow"
    depends_on:
-      - build-release-image
+      - create-multi-arch-manifest
      - build-wheel-cuda-12-8
      - build-wheel-cuda-12-6
-      - build-wheel-cuda-11-8
+      - build-wheel-cuda-12-9
    id: annotate-release-workflow
    agents:
      queue: cpu_queue_postmerge
@ -106,19 +149,3 @@ steps:
      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
    env:
      DOCKER_BUILDKIT: "1"
-
-  - block: "Build Neuron release image"
-    key: block-neuron-release-image-build
-    depends_on: ~
-
-  - label: "Build and publish Neuron release image"
-    depends_on: block-neuron-release-image-build
-    agents:
-      queue: neuron-postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
-    env:
-      DOCKER_BUILDKIT: "1"
--- a/.buildkite/scripts/hardware_ci/run-amd-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-amd-test.sh
@ -121,7 +121,6 @@ fi
 if [[ $commands == *" kernels/quantization"* ]]; then
  commands="${commands} \
  --ignore=kernels/quantization/test_int8_quant.py \
-  --ignore=kernels/quantization/test_aqlm.py \
  --ignore=kernels/quantization/test_machete_mm.py \
  --ignore=kernels/quantization/test_block_fp8.py \
  --ignore=kernels/quantization/test_block_int8.py \
@ -165,7 +164,6 @@ if [[ $commands == *" entrypoints/llm "* ]]; then
  --ignore=entrypoints/llm/test_chat.py \
  --ignore=entrypoints/llm/test_accuracy.py \
  --ignore=entrypoints/llm/test_init.py \
-  --ignore=entrypoints/llm/test_generate_multiple_loras.py \
  --ignore=entrypoints/llm/test_prompt_validation.py "}
 fi

--- a/.buildkite/scripts/hardware_ci/run-cpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-cpu-test.sh
@ -25,8 +25,8 @@ numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE
 numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .

 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2

 function cpu_tests() {
  set -e
@ -46,21 +46,26 @@ function cpu_tests() {
    set -e
    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"

+  # Run kernel tests
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -x -v -s tests/kernels/test_onednn.py"
+
  # Run basic model test
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    # Note: disable until supports V1
-    # pytest -v -s tests/kernels/attention/test_cache.py -m cpu_model
-    # pytest -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
+    # pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
+    # pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model

    # Note: disable Bart until supports V1
-    pytest -v -s tests/models/language/generation -m cpu_model \
+    pytest -x -v -s tests/models/language/generation -m cpu_model \
                --ignore=tests/models/language/generation/test_bart.py
-    VLLM_CPU_SGL_KERNEL=1 pytest -v -s tests/models/language/generation -m cpu_model \
+    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model \
                --ignore=tests/models/language/generation/test_bart.py

-    pytest -v -s tests/models/language/pooling -m cpu_model
-    pytest -v -s tests/models/multimodal/generation \
+    pytest -x -v -s tests/models/language/pooling -m cpu_model
+    pytest -x -v -s tests/models/multimodal/generation \
                --ignore=tests/models/multimodal/generation/test_mllama.py \
                --ignore=tests/models/multimodal/generation/test_pixtral.py \
                -m cpu_model"
@ -68,35 +73,51 @@ function cpu_tests() {
  # Run compressed-tensor test
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
-    pytest -s -v \
+    pytest -x -s -v \
    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"

  # Note: disable it until supports V1
  # Run AWQ test
  # docker exec cpu-test-"$NUMA_NODE" bash -c "
  #   set -e
-  #   VLLM_USE_V1=0 pytest -s -v \
+  #   VLLM_USE_V1=0 pytest -x -s -v \
  #   tests/quantization/test_ipex_quant.py"

  # Run multi-lora tests
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
-    pytest -s -v \
+    pytest -x -s -v \
    tests/lora/test_qwen2vl.py"

-  # online serving
+  # online serving: tp+pp
  docker exec cpu-test-"$NUMA_NODE" bash -c '
    set -e
    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
+    server_pid=$!
    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
    vllm bench serve \
      --backend vllm \
      --dataset-name random \
      --model meta-llama/Llama-3.2-3B-Instruct \
      --num-prompts 20 \
-      --endpoint /v1/completions'
+      --endpoint /v1/completions
+    kill -s SIGTERM $server_pid &'
+
+  # online serving: tp+dp
+  docker exec cpu-test-"$NUMA_NODE" bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
+    server_pid=$!
+    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+    vllm bench serve \
+      --backend vllm \
+      --dataset-name random \
+      --model meta-llama/Llama-3.2-3B-Instruct \
+      --num-prompts 20 \
+      --endpoint /v1/completions
+    kill -s SIGTERM $server_pid &'
 }

 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
-timeout 1.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+timeout 2h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
--- a/.buildkite/scripts/hardware_ci/run-neuron-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-neuron-test.sh
@ -1,64 +0,0 @@
-#!/bin/bash
-
-# This script build the Neuron docker image and run the API server inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -e
-set -v
-
-image_name="neuron/vllm-ci"
-container_name="neuron_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-
-HF_CACHE="$(realpath ~)/huggingface"
-mkdir -p "${HF_CACHE}"
-HF_MOUNT="/root/.cache/huggingface"
-HF_TOKEN=$(aws secretsmanager get-secret-value  --secret-id "ci/vllm-neuron/hf-token" --region us-west-2 --query 'SecretString' --output text | jq -r .VLLM_NEURON_CI_HF_TOKEN)
-
-NEURON_COMPILE_CACHE_URL="$(realpath ~)/neuron_compile_cache"
-mkdir -p "${NEURON_COMPILE_CACHE_URL}"
-NEURON_COMPILE_CACHE_MOUNT="/root/.cache/neuron_compile_cache"
-
-# Try building the docker image
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws
-
-# prune old image and containers to save disk space, and only once a day
-# by using a timestamp file in tmp.
-if [ -f /tmp/neuron-docker-build-timestamp ]; then
-    last_build=$(cat /tmp/neuron-docker-build-timestamp)
-    current_time=$(date +%s)
-    if [ $((current_time - last_build)) -gt 86400 ]; then
-        # Remove dangling images (those that are not tagged and not used by any container)
-        docker image prune -f
-        # Remove unused volumes / force the system prune for old images as well.
-        docker volume prune -f && docker system prune -f
-        echo "$current_time" > /tmp/neuron-docker-build-timestamp
-    fi
-else
-    date "+%s" > /tmp/neuron-docker-build-timestamp
-fi
-
-docker build -t "${image_name}" -f docker/Dockerfile.neuron .
-
-# Setup cleanup
-remove_docker_container() {
-    docker image rm -f "${image_name}" || true;
-}
-trap remove_docker_container EXIT
-
-# Run the image
-docker run --rm -it --device=/dev/neuron0 --network bridge \
-       -v "${HF_CACHE}:${HF_MOUNT}" \
-       -e "HF_HOME=${HF_MOUNT}" \
-       -e "HF_TOKEN=${HF_TOKEN}" \
-       -v "${NEURON_COMPILE_CACHE_URL}:${NEURON_COMPILE_CACHE_MOUNT}" \
-       -e "NEURON_COMPILE_CACHE_URL=${NEURON_COMPILE_CACHE_MOUNT}" \
-       --name "${container_name}" \
-       ${image_name} \
-       /bin/bash -c "
-            set -e; # Exit on first error
-            python3 /workspace/vllm/examples/offline_inference/neuron.py;
-            python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
-            for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo \"Running test file: \$f\";
-                python3 -m pytest \$f -v --capture=tee-sys;
-            done
-       "
--- a/.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh
+++ b/.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh
@ -4,8 +4,7 @@ set -xu


 remove_docker_container() { 
-    docker rm -f tpu-test || true; 
-    docker rm -f vllm-tpu || true;
+    docker rm -f tpu-test || true;
 }

 trap remove_docker_container EXIT
@ -62,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
+    && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
    && python3 -m pip install --progress-bar off hf-transfer
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
@ -129,7 +128,7 @@ run_and_track_test() {

 # --- Actual Test Execution ---
 run_and_track_test 1 "test_struct_output_generate.py" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
 run_and_track_test 2 "test_moe_pallas.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
 run_and_track_test 3 "test_lora.py" \
@ -140,6 +139,8 @@ run_and_track_test 5 "test_spmd_model_weight_loading.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
 run_and_track_test 6 "test_kv_cache_update_kernel.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
+run_and_track_test 7 "test_tpu_int8.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_int8.py"

 # After all tests have been attempted, exit with the overall status.
 if [ "$overall_script_exit_code" -ne 0 ]; then
--- a/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh
@ -5,7 +5,6 @@ set -xu

 remove_docker_container() { 
    docker rm -f tpu-test || true; 
-    docker rm -f vllm-tpu || true;
 }

 trap remove_docker_container EXIT
@ -62,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
+    && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
    && python3 -m pip install --progress-bar off hf-transfer
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
@ -135,7 +134,7 @@ run_and_track_test 1 "test_compilation.py" \
 run_and_track_test 2 "test_basic.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
 run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
+    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
 run_and_track_test 5 "examples/offline_inference/tpu.py" \
--- a/.buildkite/scripts/hardware_ci/run-xpu-test.sh
+++ b/.buildkite/scripts/hardware_ci/run-xpu-test.sh
@ -23,20 +23,26 @@ docker run \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
    --name "${container_name}" \
    "${image_name}" \
-    sh -c '
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
+    bash -c '
+    set -e
+    echo $ZE_AFFINITY_MASK
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -O.cudagraph_mode=NONE
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
+    VLLM_ATTENTION_BACKEND=TRITON_ATTN_VLLM_V1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
    cd tests
    pytest -v -s v1/core
    pytest -v -s v1/engine
    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
    pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py --ignore=v1/spec_decode/test_tree_attention.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py
    pytest -v -s v1/test_serial_utils.py
    pytest -v -s v1/test_utils.py
    pytest -v -s v1/test_metrics_reader.py
--- a/.buildkite/scripts/tpu/cleanup_docker.sh
+++ b/.buildkite/scripts/tpu/cleanup_docker.sh
@ -17,7 +17,7 @@ if [ "$disk_usage" -gt "$threshold" ]; then
  # Remove dangling images (those that are not tagged and not used by any container)
  docker image prune -f
  # Remove unused volumes / force the system prune for old images as well.
-  docker volume prune -f && docker system prune --force --filter "until=72h" --all
+  docker volume prune -f && docker system prune --force --filter "until=24h" --all
  echo "Docker images and volumes cleanup completed."
 else
  echo "Disk usage is below $threshold%. No cleanup needed."
--- a/.buildkite/scripts/tpu/config_v6e_1.env
+++ b/.buildkite/scripts/tpu/config_v6e_1.env
@ -1,6 +1,6 @@
 # Environment config
 TEST_NAME=llama8b
-CONTAINER_NAME=vllm-tpu
+CONTAINER_NAME=tpu-test

 # vllm config
 MODEL=meta-llama/Llama-3.1-8B-Instruct
--- a/.buildkite/scripts/tpu/docker_run_bm.sh
+++ b/.buildkite/scripts/tpu/docker_run_bm.sh
@ -12,8 +12,6 @@ source /etc/environment
 source $ENV_FILE

 remove_docker_container() { 
-    docker rm -f tpu-test || true; 
-    docker rm -f vllm-tpu || true;
    docker rm -f $CONTAINER_NAME || true;
 }

--- a/.buildkite/scripts/tpu/quantized_v6e_1.env
+++ b/.buildkite/scripts/tpu/quantized_v6e_1.env
@ -1,6 +1,6 @@
 # Environment config
 TEST_NAME=llama8bw8a8
-CONTAINER_NAME=vllm-tpu
+CONTAINER_NAME=tpu-test

 # vllm config
 MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
--- a/.buildkite/scripts/tpu/run_bm.sh
+++ b/.buildkite/scripts/tpu/run_bm.sh
@ -44,7 +44,6 @@ echo

 VLLM_USE_V1=1 vllm serve $MODEL \
 --seed 42 \
- --disable-log-requests \
 --max-num-seqs $MAX_NUM_SEQS \
 --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
 --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
--- a/.buildkite/scripts/upload-wheels.sh
+++ b/.buildkite/scripts/upload-wheels.sh
@ -14,8 +14,19 @@ fi
 # Get the single wheel file
 wheel="${wheel_files[0]}"

-# Rename 'linux' to 'manylinux1' in the wheel filename
-new_wheel="${wheel/linux/manylinux1}"
+# Detect architecture and rename 'linux' to appropriate manylinux version
+arch=$(uname -m)
+if [[ $arch == "x86_64" ]]; then
+    manylinux_version="manylinux1"
+elif [[ $arch == "aarch64" ]]; then
+    manylinux_version="manylinux2014"
+else
+    echo "Warning: Unknown architecture $arch, using manylinux1 as default"
+    manylinux_version="manylinux1"
+fi
+
+# Rename 'linux' to the appropriate manylinux version in the wheel filename
+new_wheel="${wheel/linux/$manylinux_version}"
 mv -- "$wheel" "$new_wheel"
 wheel="$new_wheel"

@ -47,14 +58,15 @@ python3 .buildkite/generate_index.py --wheel "$normal_wheel"
 aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"

-if [[ $normal_wheel == *"cu118"* ]]; then
-    # if $normal_wheel matches cu118, do not upload the index.html
-    echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu126"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
    # if $normal_wheel matches cu126, do not upload the index.html
    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
 else
-    # only upload index.html for cu128 wheels (default wheels)
+    # only upload index.html for cu129 wheels (default wheels) as it
+    # is available on both x86 and arm64
    aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
    aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
 fi
@ -63,14 +75,15 @@ fi
 aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"

-if [[ $normal_wheel == *"cu118"* ]]; then
-    # if $normal_wheel matches cu118, do not upload the index.html
-    echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu126"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
    # if $normal_wheel matches cu126, do not upload the index.html
    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
 else
-    # only upload index.html for cu128 wheels (default wheels)
+    # only upload index.html for cu129 wheels (default wheels) as it
+    # is available on both x86 and arm64
    aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
 fi

--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@ -31,16 +31,6 @@
 steps:
 ##### fast check tests  #####

- label: Documentation Build # 2min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/test_docs"
-  fast_check: true
-  no_gpu: True
-  commands:
-  - pip install -r ../requirements/docs.txt
-  # TODO: add `--strict` once warnings in docstrings are fixed
-  - mkdocs build
-
 - label: Pytorch Nightly Dependency Override Check # 2min
  # if this test fails, it means the nightly torch version is not compatible with some
  # of the dependencies. Please check the error message and add the package to whitelist
@ -51,29 +41,31 @@ steps:
  commands:
  - bash standalone_tests/pytorch_nightly_dependency.sh

- label: Async Engine, Inputs, Utils, Worker Test # 24min
+- label: Async Engine, Inputs, Utils, Worker Test # 36min
+  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/mq_llm_engine
  - tests/async_engine
-  - tests/test_inputs
+  - tests/test_inputs.py
+  - tests/test_outputs.py
  - tests/multimodal
-  - tests/test_utils
+  - tests/utils_
  - tests/worker
  - tests/standalone_tests/lazy_imports.py
  commands:
  - python3 standalone_tests/lazy_imports.py
  - pytest -v -s mq_llm_engine # MQLLMEngine
  - pytest -v -s async_engine # AsyncLLMEngine
-  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
  - pytest -v -s test_inputs.py
  - pytest -v -s test_outputs.py
  - pytest -v -s multimodal
-  - pytest -v -s test_utils.py # Utils
+  - pytest -v -s utils_ # Utils
  - pytest -v -s worker # Worker

- label: Python-only Installation Test
+- label: Python-only Installation Test # 10min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
@ -81,7 +73,8 @@ steps:
  commands:
  - bash standalone_tests/python_only_compile.sh

- label: Basic Correctness Test # 30min
+- label: Basic Correctness Test # 20min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  fast_check: true
  torch_nightly: true
@ -98,16 +91,8 @@ steps:
  - pytest -v -s basic_correctness/test_cpu_offload.py
  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py

- label: Chunked Prefill Test
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/
-  - tests/basic_correctness/test_chunked_prefill
-  commands:
-  - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
-  - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
-
- label: Core Test # 10min
+- label: Core Test # 22min
+  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental]
  fast_check: true
  source_file_dependencies:
@ -117,7 +102,8 @@ steps:
  commands:
  - pytest -v -s core

- label: Entrypoints Test (LLM) # 40min
+- label: Entrypoints Test (LLM) # 30min
+  timeout_in_minutes: 40
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
@ -128,13 +114,13 @@ steps:
  - tests/entrypoints/offline_mode
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
  - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests

- label: Entrypoints Test (API Server) # 40min
+- label: Entrypoints Test (API Server) # 100min
+  timeout_in_minutes: 130
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
@ -145,10 +131,12 @@ steps:
  - tests/entrypoints/test_chat_utils
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/openai/test_collective_rpc.py # PYTHONPATH is needed to import custom Worker extension
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_collective_rpc.py
  - pytest -v -s entrypoints/test_chat_utils.py

- label: Distributed Tests (4 GPUs) # 10min
+- label: Distributed Tests (4 GPUs) # 35min
+  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
@ -191,7 +179,8 @@ steps:
  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
  - popd

- label: EPLB Algorithm Test
+- label: EPLB Algorithm Test # 5min
+  timeout_in_minutes: 15
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/distributed/eplb
@ -200,6 +189,7 @@ steps:
  - pytest -v -s distributed/test_eplb_algo.py

 - label: EPLB Execution Test # 5min
+  timeout_in_minutes: 15
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@ -208,7 +198,8 @@ steps:
  commands:
  - pytest -v -s distributed/test_eplb_execute.py

- label: Metrics, Tracing Test # 10min
+- label: Metrics, Tracing Test # 12min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  num_gpus: 2
  source_file_dependencies:
@ -227,7 +218,8 @@ steps:
 ##### fast check tests  #####
 #####  1 GPU test  #####

- label: Regression Test # 5min
+- label: Regression Test # 7min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
@ -237,7 +229,8 @@ steps:
  - pytest -v -s test_regression.py
  working_dir: "/vllm-workspace/tests" # optional

- label: Engine Test # 10min
+- label: Engine Test # 25min
+  timeout_in_minutes: 40
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
@ -252,7 +245,29 @@ steps:
  # OOM in the CI unless we run this separately
  - pytest -v -s tokenization

- label: V1 Test
+- label: V1 Test e2e + engine # 30min
+  timeout_in_minutes: 45
+  mirror_hardwares: [amdexperimental]
+  source_file_dependencies:
+    - vllm/
+    - tests/v1
+  commands:
+    # TODO: accuracy does not match, whether setting
+    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
+    - pytest -v -s v1/e2e
+    - pytest -v -s v1/engine
+
+- label: V1 Test entrypoints # 35min
+  timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
+  source_file_dependencies:
+    - vllm/
+    - tests/v1
+  commands:
+    - pytest -v -s v1/entrypoints
+
+- label: V1 Test others # 42min
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
    - vllm/
@ -260,9 +275,9 @@ steps:
  commands:
    # split the test to avoid interference
    - pytest -v -s v1/core
-    - pytest -v -s v1/engine
-    - pytest -v -s v1/entrypoints
+    - pytest -v -s v1/executor
    - pytest -v -s v1/sample
+    - pytest -v -s v1/logits_processors
    - pytest -v -s v1/worker
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/spec_decode
@ -272,14 +287,12 @@ steps:
    - pytest -v -s v1/test_utils.py
    - pytest -v -s v1/test_oracle.py
    - pytest -v -s v1/test_metrics_reader.py
-    # TODO: accuracy does not match, whether setting
-    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
-    - pytest -v -s v1/e2e
    # Integration test for streaming correctness (requires special branch).
    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

- label: Examples Test # 25min
+- label: Examples Test # 30min
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/examples"
  source_file_dependencies:
@ -304,16 +317,8 @@ steps:
    - python3 offline_inference/basic/score.py
    - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2

- label: Prefix Caching Test # 9min
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/
-  - tests/prefix_caching
-  commands:
-    - pytest -v -s prefix_caching
-
-
- label: Platform Tests (CUDA)
+- label: Platform Tests (CUDA) # 4min
+  timeout_in_minutes: 15
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
@ -321,7 +326,8 @@ steps:
  commands:
    - pytest -v -s cuda/test_cuda_context.py

- label: Samplers Test # 36min
+- label: Samplers Test # 56min
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor/layers
@ -332,15 +338,23 @@ steps:
    - pytest -v -s samplers
    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers

- label: LoRA Test %N # 15min each
+- label: LoRA Test %N # 20min each
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/lora
  - tests/lora
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py
+  commands:
+    - pytest -v -s lora \
+      --shard-id=$$BUILDKITE_PARALLEL_JOB \
+      --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
+      --ignore=lora/test_chatglm3_tp.py \
+      --ignore=lora/test_llama_tp.py \
+      --ignore=lora/test_llm_with_multi_loras.py
  parallelism: 4

- label: PyTorch Compilation Unit Tests
+- label: PyTorch Compilation Unit Tests # 15min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
@ -354,8 +368,10 @@ steps:
    - pytest -v -s compile/test_sequence_parallelism.py
    - pytest -v -s compile/test_async_tp.py
    - pytest -v -s compile/test_fusion_all_reduce.py
+    - pytest -v -s compile/test_decorator.py

- label: PyTorch Fullgraph Smoke Test # 9min
+- label: PyTorch Fullgraph Smoke Test # 15min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
@ -367,8 +383,10 @@ steps:
  - pytest -v -s compile/piecewise/test_simple.py
  - pytest -v -s compile/piecewise/test_toy_llama.py
  - pytest -v -s compile/piecewise/test_full_cudagraph.py
+  - pytest -v -s compile/piecewise/test_multiple_graphs.py

- label: PyTorch Fullgraph Test # 18min
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
@ -377,7 +395,8 @@ steps:
  commands:
  - pytest -v -s compile/test_full_graph.py

- label: Kernels Core Operation Test
+- label: Kernels Core Operation Test # 48min
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
@ -385,7 +404,8 @@ steps:
  commands:
    - pytest -v -s kernels/core

- label: Kernels Attention Test %N
+- label: Kernels Attention Test %N # 23min
+  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/attention/
@ -396,7 +416,8 @@ steps:
    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels Quantization Test %N
+- label: Kernels Quantization Test %N # 64min
+  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/quantization/
@ -406,17 +427,21 @@ steps:
    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels MoE Test %N
+- label: Kernels MoE Test %N # 40min
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
  - csrc/moe/
  - tests/kernels/moe
  - vllm/model_executor/layers/fused_moe/
+  - vllm/distributed/device_communicators/
  commands:
    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels Mamba Test
+- label: Kernels Mamba Test # 31min
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/mamba/
@ -424,9 +449,9 @@ steps:
  commands:
    - pytest -v -s kernels/mamba

- label: Tensorizer Test # 11min
+- label: Tensorizer Test # 14min
+  timeout_in_minutes: 25
  mirror_hardwares: [amdexperimental]
-  soft_fail: true
  source_file_dependencies:
  - vllm/model_executor/model_loader
  - tests/tensorizer_loader
@ -437,7 +462,8 @@ steps:
    - pytest -v -s tensorizer_loader
    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py

- label: Model Executor Test
+- label: Model Executor Test # 7min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor
@ -447,7 +473,8 @@ steps:
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - pytest -v -s model_executor

- label: Benchmarks # 9min
+- label: Benchmarks # 11min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/.buildkite"
  source_file_dependencies:
@ -455,7 +482,8 @@ steps:
  commands:
  - bash scripts/run-benchmarks.sh

- label: Benchmarks CLI Test # 10min
+- label: Benchmarks CLI Test # 7min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
@ -463,7 +491,8 @@ steps:
  commands:
  - pytest -v -s benchmarks/

- label: Quantization Test
+- label: Quantization Test # 70min
+  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
@ -471,21 +500,21 @@ steps:
  - tests/quantization
  commands:
  # temporary install here since we need nightly, will move to requirements/test.in
-  # after torchao 0.12 release
-  - pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
+  # after torchao 0.12 release, and pin a working version of torchao nightly here
+  - pip install --pre torchao==0.13.0.dev20250814 --index-url https://download.pytorch.org/whl/nightly/cu128
  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization

 - label: LM Eval Small Models # 53min
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1

- label: OpenAI API correctness
+- label: OpenAI API correctness # 22min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
@ -494,7 +523,8 @@ steps:
  commands: # LMEval+Transcription WER check
  - pytest -s entrypoints/openai/correctness/

- label: Encoder Decoder tests # 5min
+- label: Encoder Decoder tests # 12min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
@ -502,7 +532,8 @@ steps:
  commands:
    - pytest -v -s encoder_decoder

- label: OpenAI-Compatible Tool Use # 20 min
+- label: OpenAI-Compatible Tool Use # 23 min
+  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental]
  fast_check: false
  source_file_dependencies:
@ -515,7 +546,8 @@ steps:

 #####  models test  #####

- label: Basic Models Test # 24min
+- label: Basic Models Test # 57min
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
@ -528,30 +560,33 @@ steps:
    - pytest -v -s models/test_vision.py
    - pytest -v -s models/test_initialization.py

- label: Language Models Test (Standard)
+- label: Language Models Test (Standard) # 35min
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/language
  commands:
-    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
-    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
    - pip freeze | grep -E 'torch'
    - pytest -v -s models/language -m core_model

 - label: Language Models Test (Hybrid) # 35 min
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/generation
  commands:
-    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
-    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
+    # Install fast path packages for testing against transformers
+    # Note: also needed to run plamo2 model in vLLM
+    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
    - pytest -v -s models/language/generation -m hybrid_model

- label: Language Models Test (Extended Generation) # 1hr20min
+- label: Language Models Test (Extended Generation) # 80min
+  timeout_in_minutes: 110
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
@ -563,6 +598,7 @@ steps:
    - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'

 - label: Language Models Test (Extended Pooling)  # 36min
+  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
@ -571,7 +607,17 @@ steps:
  commands:
    - pytest -v -s models/language/pooling -m 'not core_model'

- label: Multi-Modal Models Test (Standard)
+- label: Multi-Modal Processor Test # 44min
+  timeout_in_minutes: 60
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal/processing
+
+- label: Multi-Modal Models Test (Standard) # 60min
+  timeout_in_minutes: 80
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
@ -580,8 +626,7 @@ steps:
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pip freeze | grep -E 'torch'
-    - pytest -v -s models/multimodal/processing
-    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
+    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
    - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work

 - label: Multi-Modal Models Test (Extended) 1
@ -592,7 +637,7 @@ steps:
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing models/multimodal -m 'not core_model'
+    - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing

 - label: Multi-Modal Models Test (Extended) 2
  mirror_hardwares: [amdexperimental]
@ -614,7 +659,8 @@ steps:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'

- label: Quantized Models Test
+- label: Quantized Models Test # 45 min
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor/layers/quantization
@ -644,7 +690,8 @@ steps:
    - python3 examples/offline_inference/audio_language.py --model-type whisper
    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl

- label: Blackwell Test
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/"
  gpu: b200
  # optional: true
@ -655,28 +702,38 @@ steps:
  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
+  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
  - vllm/v1/attention/backends/flashinfer.py
  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
  commands:
    - nvidia-smi
    - python3 examples/offline_inference/basic/chat.py
    # Attention
    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
-    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_decode_attention.py
+    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
    - pytest -v -s tests/kernels/test_cutlass_mla_decode.py
    # Quantization
    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
+    - pytest -v -s tests/kernels/quantization/test_silu_nvfp4_quant_fusion.py
    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
+    - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
+    - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
+    - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py
    # Fusion
    - pytest -v -s tests/compile/test_fusion_all_reduce.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py

 #####  1 GPU test  #####
 #####  multi gpus test  #####

 - label: Distributed Comm Ops Test # 7min
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
@ -688,6 +745,7 @@ steps:
  - pytest -v -s distributed/test_shm_broadcast.py

 - label: 2 Node Tests (4 GPUs in total) # 16min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
@ -711,7 +769,8 @@ steps:
    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code

- label: Distributed Tests (2 GPUs) # 40min
+- label: Distributed Tests (2 GPUs) # 110min
+  timeout_in_minutes: 150
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
@ -748,11 +807,11 @@ steps:
  # this test fails consistently.
  # TODO: investigate and fix
  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
-  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
  - pytest -v -s models/multimodal/generation/test_maverick.py

 - label: Plugin Tests (2 GPUs) # 40min
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
@ -765,6 +824,11 @@ steps:
  - pytest -v -s plugins_tests/test_platform_plugins.py
  - pip uninstall vllm_add_dummy_platform -y
  # end platform plugin tests
+  # begin io_processor plugins test, all the code in between uses the prithvi_io_processor plugin
+  - pip install -e ./plugins/prithvi_io_processor_plugin
+  - pytest -v -s plugins_tests/test_io_processor_plugins.py
+  - pip uninstall prithvi_io_processor_plugin -y 
+  # end io_processor plugins test
  # other tests continue here:
  - pytest -v -s plugins_tests/test_scheduler_plugins.py
  - pip install -e ./plugins/vllm_add_dummy_model
@ -773,28 +837,8 @@ steps:
  - pytest -v -s models/test_oot_registration.py # it needs a clean process
  - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins

- label: Multi-step Tests (4 GPUs) # 36min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/model_executor/layers/sampler.py
-  - vllm/sequence.py
-  - vllm/worker/worker_base.py
-  - vllm/worker/worker.py
-  - vllm/worker/multi_step_worker.py
-  - vllm/worker/model_runner_base.py
-  - vllm/worker/model_runner.py
-  - vllm/worker/multi_step_model_runner.py
-  - vllm/engine
-  - tests/multi_step
-  commands:
-  # this test is quite flaky
-  # TODO: investigate and fix.
-  # - pytest -v -s multi_step/test_correctness_async_llm.py
-  - pytest -v -s multi_step/test_correctness_llm.py
-
- label: Pipeline Parallelism Test # 45min
+- label: Pipeline + Context Parallelism Test # 45min
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
@ -807,8 +851,10 @@ steps:
  commands:
  - pytest -v -s distributed/test_pp_cudagraph.py
  - pytest -v -s distributed/test_pipeline_parallel.py
+  # - pytest -v -s distributed/test_context_parallel.py # TODO: enable it on Hopper runners or add triton MLA support

- label: LoRA TP Test (Distributed)
+- label: LoRA TP Test (Distributed) # 17 min
+  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  num_gpus: 4
  source_file_dependencies:
@ -822,13 +868,15 @@ steps:
    # requires multi-GPU testing for validation.
    - pytest -v -s -x lora/test_chatglm3_tp.py
    - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_multi_loras_with_tp.py
+    - pytest -v -s -x lora/test_llm_with_multi_loras.py


 - label: Weight Loading Multiple GPU Test  # 33min
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  num_gpus: 2 
+  optional: true
  source_file_dependencies:
  - vllm/
  - tests/weight_loading
@ -876,3 +924,10 @@ steps:
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+
+- label: Qwen MoE EP Test # optional
+  gpu: h200
+  optional: true
+  num_gpus: 2
+  commands:
+    - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 /vllm-workspace/examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@ -5,12 +5,15 @@
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/core @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/engine/llm_engine.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
+/vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
+/vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
+/vllm/model_executor/layers/mamba @tdoublep
+/vllm/model_executor/model_loader @22quinn
 /vllm/multimodal @DarkLight1337 @ywang96
+/vllm/v1/sample @22quinn @houseroad
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm
@ -20,31 +23,32 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson

 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
-/vllm/config.py @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor
+/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg

 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
-/vllm/v1/structured_output @mgoin @russellb @aarnphm
+/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
+/vllm/v1/spec_decode @benchislett @luccafong
+/vllm/v1/attention/backends/triton_attn.py @tdoublep

 # Test ownership
 /.buildkite/lm-eval-harness @mgoin @simon-mo
 /tests/async_engine @njhill @robertgshaw2-redhat @simon-mo
-/tests/basic_correctness/test_chunked_prefill @rkooo567 @comaniac
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
-/tests/kernels @tlrmchlsmth @WoosukKwon
+/tests/kernels @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/models @DarkLight1337 @ywang96
-/tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
-/tests/quantization @mgoin @robertgshaw2-redhat
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
-/tests/weight_loading @mgoin @youkaichao
+/tests/weight_loading @mgoin @youkaichao @yewentao256
 /tests/lora @jeejeelee
+/tests/models/language/generation/test_hybrid.py @tdoublep

 # Docs
 /docs @hmellor
@ -66,6 +70,9 @@ mkdocs.yaml @hmellor
 /vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
 /vllm/model_executor/models/qwen* @sighingnow

+# MTP-specific files
+/vllm/model_executor/models/deepseek_mtp.py @luccafong
+
 # Mistral-specific files
 /vllm/model_executor/models/mistral*.py @patrickvonplaten
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten
@ -73,3 +80,14 @@ mkdocs.yaml @hmellor
 /vllm/model_executor/models/pixtral*.py @patrickvonplaten
 /vllm/transformers_utils/configs/mistral.py @patrickvonplaten
 /vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
+
+# Kernels
+/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
+/vllm/attention/ops/triton_unified_attention.py @tdoublep
+
+# ROCm related: specify owner with write access to notify AMD folks for careful code review
+/docker/Dockerfile.rocm* @gshtras
+/vllm/v1/attention/backends/rocm*.py @gshtras
+/vllm/v1/attention/backends/mla/rocm*.py @gshtras
+/vllm/attention/ops/rocm*.py @gshtras
+/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@ -1,11 +1,5 @@
-# Essential Elements of an Effective PR Description Checklist
-
- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
- [ ] The test plan, such as providing test command.
- [ ] The test results, such as pasting the results comparison before and after, or e2e results
- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-
-PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE BEEN CONSIDERED.
+<!-- markdownlint-disable -->
+PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.

 ## Purpose

@ -13,6 +7,15 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE B

 ## Test Result

-## (Optional) Documentation Update
+---
+<details>
+<summary> Essential Elements of an Effective PR Description Checklist </summary>
+
+- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
+- [ ] The test plan, such as providing test command.
+- [ ] The test results, such as pasting the results comparison before and after, or e2e results
+- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
+- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft in the [Google Doc](https://docs.google.com/document/d/1YyVqrgX4gHTtrstbq8oWUImOyPCKSGnJ7xtTpmXzlRs/edit?tab=t.0).
+</details>

 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@ -118,6 +118,20 @@ pull_request_rules:
      add:
        - qwen

+- name: label-gpt-oss
+  description: Automatically apply gpt-oss label
+  conditions:
+    - or:
+      - files~=^examples/.*gpt[-_]?oss.*\.py
+      - files~=^tests/.*gpt[-_]?oss.*\.py
+      - files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
+      - files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
+      - title~=(?i)gpt[-_]?oss
+  actions:
+    label:
+      add:
+        - gpt-oss
+
 - name: label-rocm
  description: Automatically apply rocm label
  conditions:
--- a/.github/scale-config.yml
+++ b/.github/scale-config.yml
@ -0,0 +1,21 @@
+# scale-config.yml:
+#   Powers what instance types are available for GHA auto-scaled
+#   runners. Runners listed here will be available as self hosted
+#   runners, configuration is directly pulled from the main branch.
+# runner_types:
+#   runner_label:
+#     instance_type: m4.large
+#     os: linux
+#     # min_available defaults to the global cfg in the ALI Terraform
+#     min_available: undefined
+#     # when max_available value is not defined, no max runners is enforced
+#     max_available: undefined
+#     disk_size: 50
+#     is_ephemeral: true
+
+runner_types:
+  linux.2xlarge:
+    disk_size: 150
+    instance_type: c5.2xlarge
+    is_ephemeral: true
+    os: linux
--- a/.github/scripts/cleanup_pr_body.sh
+++ b/.github/scripts/cleanup_pr_body.sh
@ -15,11 +15,11 @@ NEW=/tmp/new_pr_body.txt
 gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
 cp "${OLD}" "${NEW}"

-# Remove "FIX #xxxx (*link existing issues this PR will resolve*)"
-sed -i '/FIX #xxxx.*$/d' "${NEW}"
+# Remove markdown comments (like the <!-- markdownlint-disable --> at the start)
+sed -i '/<!--.*-->$/d' "${NEW}"

-# Remove "FILL IN THE PR DESCRIPTION HERE"
-sed -i '/FILL IN THE PR DESCRIPTION HERE/d' "${NEW}"
+# Remove "PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED."
+sed -i '/PLEASE FILL IN THE PR DESCRIPTION HERE.*$/d' "${NEW}"

 # Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
 sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"
--- a/.github/workflows/issue_autolabel.yml
+++ b/.github/workflows/issue_autolabel.yml
@ -0,0 +1,309 @@
+name: Label issues based on keywords
+on:
+  issues:
+    types: [opened, edited, reopened]
+permissions:
+  issues: write          # needed so the workflow can add labels
+  contents: read
+concurrency:
+  group: issue-labeler-${{ github.event.issue.number }}
+  cancel-in-progress: true
+jobs:
+  add-labels:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Label issues based on keywords
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea  # v7.0.1
+        with:
+          script: |
+            // Configuration: Add new labels and keywords here
+            const labelConfig = {
+              rocm: {
+                // Keyword search - matches whole words only (with word boundaries)
+                keywords: [
+                  {
+                    term: "composable kernel",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "rccl",
+                    searchIn: "body"  // only search in body
+                  },
+                  {
+                    term: "migraphx",
+                    searchIn: "title"  // only search in title
+                  },
+                  {
+                    term: "hipgraph",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "ROCm System Management Interface",
+                    searchIn: "body"
+                  },
+                ],
+                
+                // Substring search - matches anywhere in text (partial matches)
+                substrings: [
+                  {
+                    term: "VLLM_ROCM_",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "aiter",
+                    searchIn: "title"
+                  },
+                  {
+                    term: "rocm",
+                    searchIn: "title"
+                  },
+                  {
+                    term: "amd",
+                    searchIn: "title"
+                  },
+                  {
+                    term: "hip-",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "gfx",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "cdna",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "rdna",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "torch_hip",
+                    searchIn: "body"  // only in body
+                  },
+                  {
+                    term: "_hip",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "hip_",
+                    searchIn: "both"
+                  },
+                  
+                  // ROCm tools and libraries
+                  {
+                    term: "hipify",
+                    searchIn: "both"
+                  },
+                ],
+                
+                // Regex patterns - for complex pattern matching
+                regexPatterns: [
+                  {
+                    pattern: "\\bmi\\d{3}[a-z]*\\b",
+                    description: "AMD GPU names (mi + 3 digits + optional letters)",
+                    flags: "gi",
+                    searchIn: "both"  // "title", "body", or "both"
+                  }
+                ],
+              },
+            };
+            
+            // Helper function to create regex based on search type
+            function createSearchRegex(term, type) {
+              // Escape special regex characters in the term
+              const escapedTerm = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
+              
+              switch (type) {
+                case 'keyword':
+                  // Word boundary search - matches whole words only
+                  return new RegExp(`\\b${escapedTerm}\\b`, "gi");
+                case 'substring':
+                  // Substring search - matches anywhere in the text
+                  return new RegExp(escapedTerm, "gi");
+                default:
+                  throw new Error(`Unknown search type: ${type}`);
+              }
+            }
+            
+            // Helper function to find matching terms in text with line information
+            function findMatchingTermsWithLines(text, searchTerms = [], searchType = 'keyword', searchLocation = '') {
+              const matches = [];
+              const lines = text.split('\n');
+              
+              for (const termConfig of searchTerms) {
+                let regex;
+                let term, searchIn, pattern, description, flags;
+                
+                // Handle different input formats (string or object)
+                if (typeof termConfig === 'string') {
+                  term = termConfig;
+                  searchIn = 'both'; // default
+                } else {
+                  term = termConfig.term;
+                  searchIn = termConfig.searchIn || 'both';
+                  pattern = termConfig.pattern;
+                  description = termConfig.description;
+                  flags = termConfig.flags;
+                }
+                
+                // Skip if this term shouldn't be searched in the current location
+                if (searchIn !== 'both' && searchIn !== searchLocation) {
+                  continue;
+                }
+                
+                // Create appropriate regex
+                if (searchType === 'regex') {
+                  regex = new RegExp(pattern, flags || "gi");
+                } else {
+                  regex = createSearchRegex(term, searchType);
+                }
+                
+                const termMatches = [];
+                
+                // Check each line for matches
+                lines.forEach((line, lineIndex) => {
+                  const lineMatches = line.match(regex);
+                  if (lineMatches) {
+                    lineMatches.forEach(match => {
+                      termMatches.push({
+                        match: match,
+                        lineNumber: lineIndex + 1,
+                        lineContent: line.trim(),
+                        searchType: searchType,
+                        searchLocation: searchLocation,
+                        originalTerm: term || pattern,
+                        description: description,
+                        // Show context around the match in the line
+                        context: line.length > 100 ? 
+                          line.substring(Math.max(0, line.toLowerCase().indexOf(match.toLowerCase()) - 30), 
+                                       line.toLowerCase().indexOf(match.toLowerCase()) + match.length + 30) + '...' 
+                          : line.trim()
+                      });
+                    });
+                  }
+                });
+                
+                if (termMatches.length > 0) {
+                  matches.push({
+                    term: term || (description || pattern),
+                    searchType: searchType,
+                    searchLocation: searchLocation,
+                    searchIn: searchIn,
+                    pattern: pattern,
+                    matches: termMatches,
+                    count: termMatches.length
+                  });
+                }
+              }
+              
+              return matches;
+            }
+            
+            // Helper function to check if label should be added
+            async function processLabel(labelName, config) {
+              const body = context.payload.issue.body || "";
+              const title = context.payload.issue.title || "";
+              
+              core.notice(`Processing label: ${labelName}`);
+              core.notice(`Issue Title: "${title}"`);
+              core.notice(`Issue Body length: ${body.length} characters`);
+              
+              let shouldAddLabel = false;
+              let allMatches = [];
+              let reason = '';
+              
+              const keywords = config.keywords || [];
+              const substrings = config.substrings || [];
+              const regexPatterns = config.regexPatterns || [];
+              
+              core.notice(`Searching with ${keywords.length} keywords, ${substrings.length} substrings, and ${regexPatterns.length} regex patterns`);
+              
+              // Search in title
+              if (title.trim()) {
+                core.notice(`Searching in title: "${title}"`);
+                
+                const titleKeywordMatches = findMatchingTermsWithLines(title, keywords, 'keyword', 'title');
+                const titleSubstringMatches = findMatchingTermsWithLines(title, substrings, 'substring', 'title');
+                const titleRegexMatches = findMatchingTermsWithLines(title, regexPatterns, 'regex', 'title');
+                
+                allMatches.push(...titleKeywordMatches, ...titleSubstringMatches, ...titleRegexMatches);
+              }
+              
+              // Search in body
+              if (body.trim()) {
+                core.notice(`Searching in body (${body.length} characters)`);
+                
+                const bodyKeywordMatches = findMatchingTermsWithLines(body, keywords, 'keyword', 'body');
+                const bodySubstringMatches = findMatchingTermsWithLines(body, substrings, 'substring', 'body');
+                const bodyRegexMatches = findMatchingTermsWithLines(body, regexPatterns, 'regex', 'body');
+                
+                allMatches.push(...bodyKeywordMatches, ...bodySubstringMatches, ...bodyRegexMatches);
+              }
+              
+              if (allMatches.length > 0) {
+                core.notice(`Found ${allMatches.length} matching term(s):`);
+                
+                for (const termMatch of allMatches) {
+                  const locationText = termMatch.searchLocation === 'title' ? 'title' : 'body';
+                  const searchInText = termMatch.searchIn === 'both' ? 'both' : termMatch.searchIn;
+                  
+                  if (termMatch.searchType === 'regex') {
+                    core.notice(`  📍 Regex: "${termMatch.term}" (pattern: ${termMatch.pattern}) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
+                  } else {
+                    core.notice(`  📍 Term: "${termMatch.term}" (${termMatch.searchType} search) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
+                  }
+                  
+                  // Show details for each match
+                  termMatch.matches.forEach((match, index) => {
+                    core.notice(`    ${index + 1}. Line ${match.lineNumber} in ${match.searchLocation}: "${match.match}" [${match.searchType}]`);
+                    if (match.description) {
+                      core.notice(`       Description: ${match.description}`);
+                    }
+                    core.notice(`       Context: ${match.context}`);
+                    if (match.lineContent !== match.context) {
+                      core.notice(`       Full line: ${match.lineContent}`);
+                    }
+                  });
+                }
+                
+                shouldAddLabel = true;
+                const totalMatches = allMatches.reduce((sum, t) => sum + t.count, 0);
+                const titleMatches = allMatches.filter(t => t.searchLocation === 'title').reduce((sum, t) => sum + t.count, 0);
+                const bodyMatches = allMatches.filter(t => t.searchLocation === 'body').reduce((sum, t) => sum + t.count, 0);
+                const keywordMatches = allMatches.filter(t => t.searchType === 'keyword').reduce((sum, t) => sum + t.count, 0);
+                const substringMatches = allMatches.filter(t => t.searchType === 'substring').reduce((sum, t) => sum + t.count, 0);
+                const regexMatches = allMatches.filter(t => t.searchType === 'regex').reduce((sum, t) => sum + t.count, 0);
+                
+                reason = `Found ${totalMatches} total matches (${titleMatches} in title, ${bodyMatches} in body) - ${keywordMatches} keyword matches, ${substringMatches} substring matches, ${regexMatches} regex matches`;
+              }
+              
+              core.notice(`Final decision: ${shouldAddLabel ? 'ADD LABEL' : 'DO NOT ADD LABEL'}`);
+              core.notice(`Reason: ${reason || 'No matching terms found'}`);
+              
+              if (shouldAddLabel) {
+                const existingLabels = context.payload.issue.labels.map(l => l.name);
+                if (!existingLabels.includes(labelName)) {
+                  await github.rest.issues.addLabels({
+                    owner: context.repo.owner,
+                    repo: context.repo.repo,
+                    issue_number: context.issue.number,
+                    labels: [labelName],
+                  });
+                  core.notice(`Label "${labelName}" added. ${reason}`);
+                  return true;
+                }
+                core.notice(`Label "${labelName}" already present.`);
+                return false;
+              }
+              
+              core.notice(`No matching terms found for label "${labelName}".`);
+              return false;
+            }
+            
+            // Process all configured labels
+            const processLabels = Object.entries(labelConfig)
+              .map(([labelName, config]) => processLabel(labelName, config));
+            const labelsAdded = await Promise.all(processLabels);
+            const numLabelsAdded = labelsAdded.reduce((x, y) => x + y, 0);
+            core.notice(`Processing complete. ${numLabelsAdded} label(s) added.`);
--- a/.github/workflows/lint-and-deploy.yaml
+++ b/.github/workflows/lint-and-deploy.yaml
@ -1,89 +0,0 @@
-name: Lint and Deploy Charts
-
-on: pull_request
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  lint-and-deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          fetch-depth: 0
-
-      - name: Set up Helm
-        uses: azure/setup-helm@b9e51907a09c216f16ebe8536097933489208112 # v4.3.0
-        with:
-          version: v3.14.4
-
-       #Python is required because ct lint runs Yamale and yamllint which require Python.
-      - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
-        with:
-          python-version: '3.13'
-
-      - name: Set up chart-testing
-        uses: helm/chart-testing-action@0d28d3144d3a25ea2cc349d6e59901c4ff469b3b # v2.7.0
-        with:
-          version: v3.10.1
-
-      - name: Run chart-testing (lint)
-        run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/online_serving/chart-helm --charts examples/online_serving/chart-helm
-
-      - name: Setup minio
-        run: |
-          docker network create vllm-net
-          docker run -d -p 9000:9000 --name minio --net vllm-net \
-                     -e "MINIO_ACCESS_KEY=minioadmin" \
-                     -e "MINIO_SECRET_KEY=minioadmin" \
-                     -v /tmp/data:/data \
-                     -v /tmp/config:/root/.minio \
-                     minio/minio server /data
-          export AWS_ACCESS_KEY_ID=minioadmin
-          export AWS_SECRET_ACCESS_KEY=minioadmin
-          export AWS_EC2_METADATA_DISABLED=true
-          mkdir opt-125m
-          cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd ..
-          aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket
-          aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive
-
-      - name: Create kind cluster
-        uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
-
-      - name: Build the Docker image vllm cpu
-        run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
-
-      - name: Configuration of docker images, network and namespace for the kind cluster
-        run: |
-          docker pull amazon/aws-cli:2.6.4
-          kind load docker-image  amazon/aws-cli:2.6.4 --name chart-testing
-          kind load docker-image vllm-cpu-env:latest --name chart-testing
-          docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")"
-          kubectl create ns ns-vllm
-
-      - name: Run chart-testing (install)
-        run: |
-          export AWS_ACCESS_KEY_ID=minioadmin
-          export AWS_SECRET_ACCESS_KEY=minioadmin
-          sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
-          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set image.env[2].name=VLLM_CPU_CI_ENV --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string image.env[2].value="1" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
-
-      - name: curl test
-        run: |
-          kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 &
-          sleep 10
-          CODE="$(curl -v -f --location http://localhost:8001/v1/completions \
-                  --header "Content-Type: application/json" \
-                  --data '{
-                          "model": "opt-125m",
-                          "prompt": "San Francisco is a",
-                          "max_tokens": 7,
-                          "temperature": 0
-                  }'):$CODE"
-          echo "$CODE"
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@ -1,111 +0,0 @@
-# This workflow will upload a Python Package to Release asset
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions
-
-name: Create Release
-
-on:
-  push:
-    tags:
-      - v*
-
-# Needed to create release and upload assets
-permissions:
-  contents: write
-
-jobs:
-  release:
-    # Retrieve tag and create release
-    name: Create Release
-    runs-on: ubuntu-latest
-    outputs:
-      upload_url: ${{ steps.create_release.outputs.upload_url }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-
-      - name: Extract branch info
-        shell: bash
-        run: |
-          echo "release_tag=${GITHUB_REF#refs/*/}" >> "$GITHUB_ENV"
-
-      - name: Create Release
-        id: create_release
-        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
-        env:
-          RELEASE_TAG: ${{ env.release_tag }}
-        with:
-          github-token: "${{ secrets.GITHUB_TOKEN }}"
-          script: |
-            const script = require('.github/workflows/scripts/create_release.js')
-            await script(github, context, core)
-
-  # NOTE(simon): No longer build wheel using GitHub Actions. See buildkite's release workflow. 
-  # wheel:
-  #   name: Build Wheel
-  #   runs-on: ${{ matrix.os }}
-  #   needs: release
-
-  #   strategy:
-  #     fail-fast: false
-  #     matrix:
-  #         os: ['ubuntu-20.04']
-  #         python-version: ['3.9', '3.10', '3.11', '3.12']
-  #         pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements/cuda.txt.
-  #         cuda-version: ['11.8', '12.1']
-
-  #   steps:
-  #     - name: Checkout
-  #       uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-
-  #     - name: Setup ccache
-  #       uses: hendrikmuhs/ccache-action@ed74d11c0b343532753ecead8a951bb09bb34bc9 # v1.2.14
-  #       with:
-  #         create-symlink: true
-  #         key: ${{ github.job }}-${{ matrix.python-version }}-${{ matrix.cuda-version }}
-
-  #     - name: Set up Linux Env
-  #       if: ${{ runner.os == 'Linux' }}
-  #       run: |
-  #         bash -x .github/workflows/scripts/env.sh
-
-  #     - name: Set up Python
-  #       uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
-  #       with:
-  #           python-version: ${{ matrix.python-version }}
-
-  #     - name: Install CUDA ${{ matrix.cuda-version }}
-  #       run: |
-  #         bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
-
-  #     - name: Install PyTorch ${{ matrix.pytorch-version }} with CUDA ${{ matrix.cuda-version }}
-  #       run: |
-  #         bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.pytorch-version }} ${{ matrix.cuda-version }}
-
-  #     - name: Build wheel
-  #       shell: bash
-  #       env:
-  #         CMAKE_BUILD_TYPE: Release # do not compile with debug symbol to reduce wheel size
-  #       run: |
-  #         bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
-  #         wheel_name=$(find dist -name "*whl" -print0 | xargs -0 -n 1 basename)
-  #         asset_name=${wheel_name//"linux"/"manylinux1"}
-  #         echo "wheel_name=${wheel_name}" >> "$GITHUB_ENV"
-  #         echo "asset_name=${asset_name}" >> "$GITHUB_ENV"
-
-  #     - name: Upload Release Asset
-  #       uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5 # v1.0.2
-  #       env:
-  #         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  #       with:
-  #         upload_url: ${{ needs.release.outputs.upload_url }}
-  #         asset_path: ./dist/${{ env.wheel_name }}
-  #         asset_name: ${{ env.asset_name }}
-  #         asset_content_type: application/*
-
-      # (Danielkinz): This last step will publish the .whl to pypi. Warning: untested
-      # - name: Publish package
-      #   uses: pypa/gh-action-pypi-publish@release/v1.8
-      #   with:
-      #     repository-url: https://test.pypi.org/legacy/
-      #     password: ${{ secrets.PYPI_API_TOKEN }}
-      #     skip-existing: true
--- a/.github/workflows/reminder_comment.yml
+++ b/.github/workflows/reminder_comment.yml
@ -12,16 +12,43 @@ jobs:
        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
        with:
          script: |
-            github.rest.issues.createComment({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              body: '👋 Hi! Thank you for contributing to the vLLM project.\n\n' +
-                '💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.\n\n' +
-                'Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your `fastcheck` build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping `simon-mo` or `khluu` to add you in our Buildkite org.\n\n' +
-                'Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n' +
-                'To run CI, PR reviewers can either: Add `ready` label to the PR or enable auto-merge.\n\n' +
-                '🚀'
-            })
+            try {
+              // Get the PR author
+              const prAuthor = context.payload.pull_request.user.login;
+              
+              // Check if this is the author's first PR in this repository
+              // Use GitHub's search API to find all PRs by this author
+              const { data: searchResults } = await github.rest.search.issuesAndPullRequests({
+                q: `repo:${context.repo.owner}/${context.repo.repo} type:pr author:${prAuthor}`,
+                per_page: 100  
+              });
+              
+              const authorPRCount = searchResults.total_count;
+              
+              console.log(`Found ${authorPRCount} PRs by ${prAuthor}`);
+              
+              // Only post comment if this is the first PR (only one PR by this author)
+              if (authorPRCount === 1) {
+                console.log(`Posting welcome comment for first-time contributor: ${prAuthor}`);
+                await github.rest.issues.createComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                body: '👋 Hi! Thank you for contributing to the vLLM project.\n\n' +
+                  '💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.\n\n' +
+                  'Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. \n\n' +
+                  'You ask your reviewers to trigger select CI tests on top of `fastcheck` CI. \n\n' +
+                  'Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n' +
+                  'To run CI, PR reviewers can either: Add `ready` label to the PR or enable auto-merge.\n\n' +
+                  'If you have any questions, please reach out to us on Slack at https://slack.vllm.ai.\n\n' +
+                  '🚀'
+                });
+              } else {
+                console.log(`Skipping comment for ${prAuthor} - not their first PR (${authorPRCount} PRs found)`);
+              }
+            } catch (error) {
+              console.error('Error checking PR history or posting comment:', error);
+              // Don't fail the workflow, just log the error
+            }
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
--- a/.gitignore
+++ b/.gitignore
@ -4,6 +4,9 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*

+# triton jit 
+.triton
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@ -147,7 +150,8 @@ venv.bak/
 # mkdocs documentation
 /site
 docs/argparse
-docs/examples
+docs/examples/*
+!docs/examples/README.md

 # mypy
 .mypy_cache/
@ -203,3 +207,6 @@ shellcheck*/

 # Ignore moe/marlin_moe gen code
 csrc/moe/marlin_moe_wna16/kernel_*
+
+# Ignore ep_kernels_workspace folder
+ep_kernels_workspace/
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -21,7 +21,7 @@ repos:
  - id: ruff-format
    files: ^(.buildkite|benchmarks|examples)/.*
 - repo: https://github.com/crate-ci/typos
-  rev: v1.34.0
+  rev: v1.35.5
  hooks:
  - id: typos
 - repo: https://github.com/PyCQA/isort
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@ -30,7 +30,7 @@ install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
-set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
+set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12" "3.13")

 # Supported AMD GPU architectures.
 set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
@ -45,8 +45,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.7.1")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.8.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.8.0")

 #
 # Try to find python package with an executable that exactly matches
@ -249,7 +249,6 @@ set(VLLM_EXT_SRC
  "csrc/quantization/gguf/gguf_kernel.cu"
  "csrc/quantization/activation_kernels.cu"
  "csrc/cuda_utils_kernels.cu"
-  "csrc/prepare_inputs/advance_step.cu"
  "csrc/custom_all_reduce.cu"
  "csrc/torch_bindings.cpp")

@ -287,7 +286,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
  FetchContent_MakeAvailable(cutlass)

  list(APPEND VLLM_EXT_SRC
-    "csrc/quantization/aqlm/gemm_kernels.cu"
    "csrc/quantization/awq/gemm_kernels.cu"
    "csrc/permute_cols.cu"
    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
@ -351,20 +349,27 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set_gencode_flags_for_srcs(
      SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
      CUDA_ARCHS "${MARLIN_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties(${MARLIN_TEMPLATE_KERNEL_SRC}
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()

    list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})

    set(MARLIN_SRCS
-       "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
       "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
-       "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
       "csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
    set_gencode_flags_for_srcs(
      SRCS "${MARLIN_SRCS}"
      CUDA_ARCHS "${MARLIN_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties("csrc/quantization/gptq_marlin/gptq_marlin.cu"
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()
    list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")
+
    message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
  else()
    message(STATUS "Not building Marlin kernels as no compatible archs found"
@ -427,6 +432,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set(SRCS
      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
    )
    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
@ -529,11 +535,32 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    endif()
  endif()

+  # The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require
+  # CUDA 12.8 or later
+  cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
+    set(SRCS
+      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
+      "csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
+      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${FP4_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
+    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
+  else()
+    message(STATUS "Not building NVFP4 as no compatible archs were found.")
+    # clear FP4_ARCHS
+    set(FP4_ARCHS)
+  endif()
+
  # FP4 Archs and flags
  cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
    set(SRCS
      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
+      "csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
      "csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
@ -541,7 +568,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
      SRCS "${SRCS}"
      CUDA_ARCHS "${FP4_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4=1")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
  else()
@ -725,6 +752,33 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                     "found in CUDA target architectures")
    endif()
  endif()
+
+  # Only build W4A8 kernels if we are building for something compatible with sm90a
+  cuda_archs_loose_intersection(W4A8_ARCHS "9.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND W4A8_ARCHS)
+    set(SRCS
+       "csrc/quantization/cutlass_w4a8/w4a8_mm_entry.cu")
+
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${W4A8_ARCHS}")
+
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+
+    message(STATUS "Building W4A8 kernels for archs: ${W4A8_ARCHS}")
+  else()
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0
+        AND W4A8_ARCHS)
+      message(STATUS "Not building W4A8 kernels as CUDA Compiler version is "
+                     "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
+                     "later if you intend on running w4a16 quantized models on "
+                     "Hopper.")
+    else()
+      message(STATUS "Not building W4A8 kernels as no compatible archs "
+                     "found in CUDA target architectures")
+    endif()
+  endif()
+
 # if CUDA endif
 endif()

@ -765,7 +819,9 @@ set(VLLM_MOE_EXT_SRC
  "csrc/moe/topk_softmax_kernels.cu")

 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
+  list(APPEND VLLM_MOE_EXT_SRC
+    "csrc/moe/moe_wna16.cu"
+    "csrc/moe/grouped_topk_kernels.cu")
 endif()

 if(VLLM_GPU_LANG STREQUAL "CUDA")
@ -834,6 +890,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    set_gencode_flags_for_srcs(
      SRCS "${MOE_WNAA16_MARLIN_SRC}"
      CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+      set_source_files_properties(${MOE_WNAA16_MARLIN_SRC}
+        PROPERTIES COMPILE_FLAGS "-static-global-template-stub=false")
+    endif()

    list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})

--- a/MANIFEST.in
+++ b/MANIFEST.in
@ -2,7 +2,6 @@ include LICENSE
 include requirements/common.txt
 include requirements/cuda.txt
 include requirements/rocm.txt
-include requirements/neuron.txt
 include requirements/cpu.txt
 include CMakeLists.txt

--- a/README.md
+++ b/README.md
@ -18,14 +18,19 @@ Easy, fast, and cheap LLM serving for everyone

 *Latest News* 🔥

- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
+- [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA).
+- [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing).
+- [2025/08] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg) focusing on building, developing, and integrating with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH).
 - [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).

 <details>
 <summary>Previous News</summary>

+- [2025/08] We hosted [vLLM Korea Meetup](https://luma.com/cgcgprmh) with Red Hat and Rebellions! We shared the latest advancements in vLLM along with project spotlights from the vLLM Korea community. Please find the meetup slides [here](https://drive.google.com/file/d/1bcrrAE1rxUgx0mjIeOWT6hNe2RefC5Hm/view).
+- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
+- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
@ -121,6 +126,7 @@ Cash Donations:

 Compute Resources:

+- Alibaba Cloud
 - AMD
 - Anyscale
 - AWS
@ -160,7 +166,7 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 ## Contact Us

 <!-- --8<-- [start:contact-us] -->
- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
+- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues)
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
--- a/SECURITY.md
+++ b/SECURITY.md
@ -42,4 +42,9 @@ For certain security issues of CRITICAL, HIGH, or MODERATE severity level, we ma

 * If you wish to be added to the prenotification group, please send an email copying all the members of the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html). Each vendor contact will be analyzed on a case-by-case basis.

+* Organizations and vendors who either ship or use vLLM, are eligible to join the prenotification group if they meet at least one of the following qualifications
+    * Substantial internal deployment leveraging the upstream vLLM project.
+    * Established internal security teams and comprehensive compliance measures.
+    * Active and consistent contributions to the upstream vLLM project.
+
 * We may withdraw organizations from receiving future prenotifications if they release fixes or any other information about issues before they are public. Group membership may also change based on policy refinements for who may be included.
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@ -22,6 +22,25 @@ become available.
      <td style="text-align: center;">✅</td>
      <td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
    </tr>
+    <tr>
+      <td><strong>ShareGPT4V (Image)</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>
+        <code>wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json</code>
+        <br>
+        <div>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:</div>
+        <code>wget http://images.cocodataset.org/zips/train2017.zip</code>
+      </td>
+    </tr>
+        <tr>
+      <td><strong>ShareGPT4Video (Video)</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>
+        <code>git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video</code>
+      </td>
+    </tr>
    <tr>
      <td><strong>BurstGPT</strong></td>
      <td style="text-align: center;">✅</td>
@ -29,7 +48,7 @@ become available.
      <td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
    </tr>
    <tr>
-      <td><strong>Sonnet</strong></td>
+      <td><strong>Sonnet (deprecated)</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td>Local file: <code>benchmarks/sonnet.txt</code></td>
@ -40,6 +59,18 @@ become available.
      <td style="text-align: center;">✅</td>
      <td><code>synthetic</code></td>
    </tr>
+    <tr>
+      <td><strong>RandomMultiModal (Image/Video)</strong></td>
+      <td style="text-align: center;">🟡</td>
+      <td style="text-align: center;">🚧</td>
+      <td><code>synthetic</code> </td>
+    </tr>
+    <tr>
+      <td><strong>Prefix Repetition</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>synthetic</code></td>
+    </tr>
    <tr>
      <td><strong>HuggingFace-VisionArena</strong></td>
      <td style="text-align: center;">✅</td>
@ -79,7 +110,12 @@ become available.

 🚧: to be supported

-**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
+**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`.
+For local `dataset-path`, please set `hf-name` to its Hugging Face ID like
+
+```bash
+--dataset-path /datasets/VisionArena-Chat/ --hf-name lmarena-ai/VisionArena-Chat
+```

 ## 🚀 Example - Online Benchmark

@ -91,7 +127,7 @@ become available.
 First start serving your model

 ```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B
 ```

 Then run the benchmarking script
@ -146,7 +182,7 @@ If the dataset you want to benchmark is not supported yet in vLLM, even then you

 ```bash
 # start server
-VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
+VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct
 ```

 ```bash
@ -171,12 +207,13 @@ You can skip applying chat template if your data already has it by using `--cust

 ```bash
 # need a model with vision capability here
-vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
+vllm serve Qwen/Qwen2-VL-7B-Instruct
 ```

 ```bash
 vllm bench serve \
  --backend openai-chat \
+  --endpoint-type openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
@ -205,7 +242,7 @@ vllm bench serve \
 ### Other HuggingFaceDataset Examples

 ```bash
-vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
+vllm serve Qwen/Qwen2-VL-7B-Instruct
 ```

 `lmms-lab/LLaVA-OneVision-Data`:
@ -213,6 +250,7 @@ vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```bash
 vllm bench serve \
  --backend openai-chat \
+  --endpoint-type openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
@ -227,6 +265,7 @@ vllm bench serve \
 ```bash
 vllm bench serve \
  --backend openai-chat \
+  --endpoint-type openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
@ -430,7 +469,7 @@ Benchmark the performance of structured output generation (JSON, grammar, regex)
 ### Server Setup

 ```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B
 ```

 ### JSON Schema Benchmark
@ -581,6 +620,20 @@ python3 benchmarks/benchmark_prefix_caching.py \
  --input-length-range 128:256
 ```

+### Prefix Repetition Dataset
+
+```bash
+vllm bench serve \
+  --backend openai \
+  --model meta-llama/Llama-2-7b-chat-hf \
+  --dataset-name prefix_repetition \
+  --num-prompts 100 \
+  --prefix-repetition-prefix-len 512 \
+  --prefix-repetition-suffix-len 128 \
+  --prefix-repetition-num-prefixes 5 \
+  --prefix-repetition-output-len 128
+```
+
 </details>

 ## ⚡ Example - Request Prioritization Benchmark
@ -616,3 +669,139 @@ python3 benchmarks/benchmark_prioritization.py \
 ```

 </details>
+
+## 👁️ Example - Multi-Modal Benchmark
+
+<details>
+<summary>Show more</summary>
+
+<br/>
+
+Benchmark the performance of multi-modal requests in vLLM.
+
+### Images (ShareGPT4V)
+
+Start vLLM:
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dtype bfloat16 \
+  --limit-mm-per-prompt '{"image": 1}' \
+  --allowed-local-media-path /path/to/sharegpt4v/images
+```
+
+Send requests with images:
+
+```bash
+python benchmarks/benchmark_serving.py \
+  --backend openai-chat \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dataset-name sharegpt \
+  --dataset-path /path/to/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json \
+  --num-prompts 100 \
+  --save-result \
+  --result-dir ~/vllm_benchmark_results \
+  --save-detailed \
+  --endpoint /v1/chat/completion
+```
+
+### Videos (ShareGPT4Video)
+
+Start vLLM:
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dtype bfloat16 \
+  --limit-mm-per-prompt '{"video": 1}' \
+  --allowed-local-media-path /path/to/sharegpt4video/videos
+```
+
+Send requests with videos:
+
+```bash
+python benchmarks/benchmark_serving.py \
+  --backend openai-chat \
+  --model Qwen/Qwen2.5-VL-7B-Instruct \
+  --dataset-name sharegpt \
+  --dataset-path /path/to/ShareGPT4Video/llava_v1_5_mix665k_with_video_chatgpt72k_share4video28k.json \
+  --num-prompts 100 \
+  --save-result \
+  --result-dir ~/vllm_benchmark_results \
+  --save-detailed \
+  --endpoint /v1/chat/completion
+```
+
+### Synthetic Random Images (random-mm)
+
+Generate synthetic image inputs alongside random text prompts to stress-test vision models without external datasets.
+
+Notes:
+
+- Works only with online benchmark via the OpenAI  backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`.
+- Video sampling is not yet implemented.
+
+Start the server (example):
+
+```bash
+vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
+  --dtype bfloat16 \
+  --max-model-len 16384 \
+  --limit-mm-per-prompt '{"image": 3, "video": 0}' \
+  --mm-processor-kwargs max_pixels=1003520
+```
+
+Benchmark. It is recommended to use the flag `--ignore-eos` to simulate real responses. You can set the size of the output via the arg `random-output-len`.
+
+Ex.1: Fixed number of items and a single image resolution, enforcing generation of approx 40 tokens:
+
+```bash
+vllm bench serve \
+  --backend openai-chat \
+  --model Qwen/Qwen2.5-VL-3B-Instruct \
+  --endpoint /v1/chat/completions \
+  --dataset-name random-mm \
+  --num-prompts 100 \
+  --max-concurrency 10 \
+  --random-prefix-len 25 \
+  --random-input-len 300 \
+  --random-output-len 40 \
+  --random-range-ratio 0.2 \
+  --random-mm-base-items-per-request 2 \
+  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
+  --random-mm-bucket-config '{(224, 224, 1): 1.0}' \
+  --request-rate inf \
+  --ignore-eos \
+  --seed 42
+```
+
+The number of items per request can be controlled by passing multiple image buckets:
+
+```bash
+  --random-mm-base-items-per-request 2 \
+  --random-mm-num-mm-items-range-ratio 0.5 \
+  --random-mm-limit-mm-per-prompt '{"image": 4, "video": 0}' \
+  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}' \
+```
+
+Flags specific to `random-mm`:
+
+- `--random-mm-base-items-per-request`: base number of multimodal items per request.
+- `--random-mm-num-mm-items-range-ratio`: vary item count uniformly in the closed integer range [floor(n·(1−r)), ceil(n·(1+r))]. Set r=0 to keep it fixed; r=1 allows 0 items.
+- `--random-mm-limit-mm-per-prompt`: per-modality hard caps, e.g. '{"image": 3, "video": 0}'.
+- `--random-mm-bucket-config`: dict mapping (H, W, T) → probability. Entries with probability 0 are removed; remaining probabilities are renormalized to sum to 1. Use T=1 for images. Set any T>1 for videos (video sampling not yet supported).
+
+Behavioral notes:
+
+- If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping.
+
+How sampling works:
+
+- Determine per-request item count k by sampling uniformly from the integer range defined by `--random-mm-base-items-per-request` and `--random-mm-num-mm-items-range-ratio`, then clamp k to at most the sum of per-modality limits.
+- For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in `--random-mm-bucket-config`, while tracking how many items of each modality have been added.
+- If a modality (e.g., image) reaches its limit from `--random-mm-limit-mm-per-prompt`, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing.
+This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`.
+- The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`.
+
+</details>
--- a/benchmarks/auto_tune/README.md
+++ b/benchmarks/auto_tune/README.md
@ -31,6 +31,12 @@ cd vllm

 You must set the following variables at the top of the script before execution.

+   Note: You can also override the default values below via environment variables when running the script.
+
+```bash
+MODEL=meta-llama/Llama-3.3-70B-Instruct SYSTEM=TPU TP=8 DOWNLOAD_DIR='' INPUT_LEN=128 OUTPUT_LEN=2048 MAX_MODEL_LEN=2300 MIN_CACHE_HIT_PCT=0 MAX_LATENCY_ALLOWED_MS=100000000000 NUM_SEQS_LIST="128 256" NUM_BATCHED_TOKENS_LIST="1024 2048 4096" VLLM_LOGGING_LEVEL=DEBUG bash auto_tune.sh
+```
+
 | Variable | Description | Example Value |
 | --- | --- | --- |
 | `BASE` | **Required.** The absolute path to the parent directory of your vLLM repository directory. | `"$HOME"` |
--- a/benchmarks/auto_tune/auto_tune.sh
+++ b/benchmarks/auto_tune/auto_tune.sh
@ -5,25 +5,41 @@

 TAG=$(date +"%Y_%m_%d_%H_%M")
 SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-BASE="$SCRIPT_DIR/../../.."
-MODEL="meta-llama/Llama-3.1-8B-Instruct"
-SYSTEM="TPU"
-TP=1
-DOWNLOAD_DIR=""
-INPUT_LEN=4000
-OUTPUT_LEN=16
-MAX_MODEL_LEN=4096
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=100000000000
-NUM_SEQS_LIST="128 256"
-NUM_BATCHED_TOKENS_LIST="512 1024 2048 4096"
+VLLM_LOGGING_LEVEL=${VLLM_LOGGING_LEVEL:-INFO}
+BASE=${BASE:-"$SCRIPT_DIR/../../.."}
+MODEL=${MODEL:-"meta-llama/Llama-3.1-8B-Instruct"}
+SYSTEM=${SYSTEM:-"TPU"}
+TP=${TP:-1}
+DOWNLOAD_DIR=${DOWNLOAD_DIR:-""}
+INPUT_LEN=${INPUT_LEN:-4000}
+OUTPUT_LEN=${OUTPUT_LEN:-16}
+MAX_MODEL_LEN=${MAX_MODEL_LEN:-4096}
+MIN_CACHE_HIT_PCT=${MIN_CACHE_HIT_PCT:-0}
+MAX_LATENCY_ALLOWED_MS=${MAX_LATENCY_ALLOWED_MS:-100000000000}
+NUM_SEQS_LIST=${NUM_SEQS_LIST:-"128 256"}
+NUM_BATCHED_TOKENS_LIST=${NUM_BATCHED_TOKENS_LIST:-"512 1024 2048 4096"}

 LOG_FOLDER="$BASE/auto-benchmark/$TAG"
 RESULT="$LOG_FOLDER/result.txt"
 PROFILE_PATH="$LOG_FOLDER/profile"

-echo "result file: $RESULT"
-echo "model: $MODEL"
+echo "====================== AUTO TUNE PARAMETERS ===================="
+echo "SCRIPT_DIR=$SCRIPT_DIR"
+echo "BASE=$BASE"
+echo "MODEL=$MODEL"
+echo "SYSTEM=$SYSTEM"
+echo "TP=$TP"
+echo "DOWNLOAD_DIR=$DOWNLOAD_DIR"
+echo "INPUT_LEN=$INPUT_LEN"
+echo "OUTPUT_LEN=$OUTPUT_LEN"
+echo "MAX_MODEL_LEN=$MAX_MODEL_LEN"
+echo "MIN_CACHE_HIT_PCT=$MIN_CACHE_HIT_PCT"
+echo "MAX_LATENCY_ALLOWED_MS=$MAX_LATENCY_ALLOWED_MS"
+echo "NUM_SEQS_LIST=$NUM_SEQS_LIST"
+echo "NUM_BATCHED_TOKENS_LIST=$NUM_BATCHED_TOKENS_LIST"
+echo "VLLM_LOGGING_LEVEL=$VLLM_LOGGING_LEVEL"
+echo "RESULT_FILE=$RESULT"
+echo "====================== AUTO TUNEPARAMETERS ===================="

 rm -rf $LOG_FOLDER
 rm -rf $PROFILE_PATH
@ -49,6 +65,7 @@ best_throughput=0
 best_max_num_seqs=0
 best_num_batched_tokens=0
 best_goodput=0
+best_request_rate=0

 start_server() {
    local gpu_memory_utilization=$1
@ -57,19 +74,35 @@ start_server() {
    local vllm_log=$4
    local profile_dir=$5

-    pkill -f vllm
+    pkill -if vllm

-    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir vllm serve $MODEL \
-        --disable-log-requests \
-        --port 8004 \
-        --gpu-memory-utilization $gpu_memory_utilization \
-        --max-num-seqs $max_num_seqs \
-        --max-num-batched-tokens $max_num_batched_tokens \
-        --tensor-parallel-size $TP \
-        --enable-prefix-caching \
-        --load-format dummy \
-        --download-dir "$DOWNLOAD_DIR" \
-        --max-model-len $MAX_MODEL_LEN > "$vllm_log" 2>&1 &
+    # Define the common arguments as a bash array.
+    # Each argument and its value are separate elements.
+    local common_args_array=(
+        "$MODEL"
+        "--disable-log-requests"
+        "--port" "8004"
+        "--gpu-memory-utilization" "$gpu_memory_utilization"
+        "--max-num-seqs" "$max_num_seqs"
+        "--max-num-batched-tokens" "$max_num_batched_tokens"
+        "--tensor-parallel-size" "$TP"
+        "--enable-prefix-caching"
+        "--load-format" "dummy"
+        "--download-dir" "$DOWNLOAD_DIR"
+        "--max-model-len" "$MAX_MODEL_LEN"
+    )
+
+    # Use the array expansion "${common_args_array[@]}"
+    # This correctly passes each element as a separate argument.
+    if [[ -n "$profile_dir" ]]; then
+        # Start server with profiling enabled
+        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
+            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
+    else
+        # Start server without profiling
+        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 \
+            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
+    fi

    # wait for 10 minutes...
    server_started=0
@ -83,6 +116,7 @@ start_server() {
            sleep 10
        fi
    done
+
    if (( ! server_started )); then
        echo "server did not start within 10 minutes. Please check server log at $vllm_log".
        return 1
@ -91,37 +125,20 @@ start_server() {
    fi
 }

-update_best_profile() {
-    local profile_dir=$1
-    local profile_index=$2
-    sorted_paths=($(find "$profile_dir" -maxdepth 1 -not -path "$profile_dir" | sort))
-    selected_profile_file=
-    if [[ "$SYSTEM" == "TPU" ]]; then
-        selected_profile_file="${sorted_paths[$profile_index]}/*.xplane.pb"
-    fi
-    if [[ "$SYSTEM" == "GPU" ]]; then
-        selected_profile_file="${sorted_paths[$profile_index]}"
-    fi
-    rm -f $PROFILE_PATH/*
-    cp $selected_profile_file $PROFILE_PATH
-}
-
 run_benchmark() {
    local max_num_seqs=$1
    local max_num_batched_tokens=$2
    local gpu_memory_utilization=$3
    echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
    local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
-    local profile_dir="$LOG_FOLDER/profile_${max_num_seqs}_${max_num_batched_tokens}"
    echo "vllm_log: $vllm_log"
    echo
    rm -f $vllm_log
-    mkdir -p $profile_dir
-    pkill -f vllm
-    local profile_index=0
+    pkill -if vllm

    echo "starting server..."
-    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log $profile_dir
+    # Call start_server without a profile_dir to avoid profiling overhead
+    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log ""
    result=$?
    if [[ "$result" -eq 1 ]]; then
        echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
@ -135,7 +152,8 @@ run_benchmark() {
    # get a basic qps by using request-rate inf
    bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-adjusted_input_len=$(( INPUT_LEN - prefix_len ))
+    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
+    # --profile flag is removed from this call
    vllm bench serve \
        --backend vllm \
        --model $MODEL  \
@ -149,8 +167,7 @@ adjusted_input_len=$(( INPUT_LEN - prefix_len ))
        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
        --num-prompts 1000 \
        --random-prefix-len $prefix_len \
-        --port 8004 \
-        --profile &> "$bm_log"
+        --port 8004 &> "$bm_log"
    throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
    e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
    goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
@ -164,7 +181,6 @@ adjusted_input_len=$(( INPUT_LEN - prefix_len ))
    # start from request-rate as int(throughput) + 1
        request_rate=$((${throughput%.*} + 1))
        while ((request_rate > 0)); do
-            profile_index=$((profile_index+1))
            # clear prefix cache
            curl -X POST http://0.0.0.0:8004/reset_prefix_cache
            sleep 5
@ -202,12 +218,7 @@ adjusted_input_len=$(( INPUT_LEN - prefix_len ))
            best_max_num_seqs=$max_num_seqs
            best_num_batched_tokens=$max_num_batched_tokens
            best_goodput=$goodput
-            if [[ "$SYSTEM" == "TPU" ]]; then
-                update_best_profile "$profile_dir/plugins/profile" $profile_index
-            fi
-            if [[ "$SYSTEM" == "GPU" ]]; then
-                update_best_profile "$profile_dir" $profile_index
-            fi
+            best_request_rate=$request_rate
        fi
    else
        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
@ -216,9 +227,9 @@ adjusted_input_len=$(( INPUT_LEN - prefix_len ))

    echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"

-    pkill vllm
+    pkill -if vllm
    sleep 10
-    printf '=%.0s' $(seq 1 20)
+    echo "===================="
    return 0
 }

@ -229,7 +240,8 @@ read -r -a num_batched_tokens_list <<< "$NUM_BATCHED_TOKENS_LIST"
 gpu_memory_utilization=0.98
 find_gpu_memory_utilization=0
 while (( $(echo "$gpu_memory_utilization >= 0.9" | bc -l) )); do
-    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log"
+    # Pass empty string for profile_dir argument
+    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log" ""
    result=$?
    if [[ "$result" -eq 0 ]]; then
        find_gpu_memory_utilization=1
@ -252,5 +264,45 @@ for num_seqs in "${num_seqs_list[@]}"; do
    done
 done
 echo "finish permutations"
+
+# =================================================================================
+# FINAL PROFILING RUN FOR THE BEST CONFIGURATION
+# =================================================================================
+if (( $(echo "$best_throughput > 0" | bc -l) )); then
+    echo
+    echo "Benchmark tuning finished. Now running profiling on the best configuration found..."
+    echo "Best config: max_num_seqs: $best_max_num_seqs, max_num_batched_tokens: $best_num_batched_tokens, throughput: $best_throughput"
+    echo
+
+    vllm_log="$LOG_FOLDER/vllm_log_BEST_PROFILE.txt"
+    bm_log="$LOG_FOLDER/bm_log_BEST_PROFILE.txt"
+
+    # Start server with the best params and profiling ENABLED
+    echo "Starting server for profiling..."
+    start_server $gpu_memory_utilization $best_max_num_seqs $best_num_batched_tokens "$vllm_log" "$PROFILE_PATH"
+
+    # Run benchmark with the best params and the --profile flag
+    echo "Running benchmark with profiling..."
+    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
+    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
+    vllm bench serve \
+        --backend vllm \
+        --model $MODEL \
+        --dataset-name random \
+        --random-input-len $adjusted_input_len \
+        --random-output-len $OUTPUT_LEN \
+        --ignore-eos \
+        --disable-tqdm \
+        --request-rate $best_request_rate \
+        --percentile-metrics ttft,tpot,itl,e2el \
+        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
+        --num-prompts 100 \
+        --random-prefix-len $prefix_len \
+        --port 8004 \
+        --profile &> "$bm_log"
+else
+    echo "No configuration met the latency requirements. Skipping final profiling run."
+fi
+pkill -if vllm
 echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH"
 echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"
--- a/benchmarks/backend_request_func.py
+++ b/benchmarks/backend_request_func.py
@ -31,9 +31,10 @@ class RequestFuncInput:
    model_name: Optional[str] = None
    logprobs: Optional[int] = None
    extra_body: Optional[dict] = None
-    multi_modal_content: Optional[dict] = None
+    multi_modal_content: Optional[dict | list[dict]] = None
    ignore_eos: bool = False
    language: Optional[str] = None
+    request_id: Optional[str] = None


@dataclass
@ -71,6 +72,9 @@ async def async_request_tgi(
            "inputs": request_func_input.prompt,
            "parameters": params,
        }
+        headers = None
+        if request_func_input.request_id:
+            headers = {"x-request-id": request_func_input.request_id}
        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
        if request_func_input.ignore_eos:
@ -82,7 +86,9 @@ async def async_request_tgi(
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
-            async with session.post(url=api_url, json=payload) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
@ -145,6 +151,9 @@ async def async_request_trt_llm(
        }
        if request_func_input.ignore_eos:
            payload["min_length"] = request_func_input.output_len
+        headers = None
+        if request_func_input.request_id:
+            headers = {"x-request-id": request_func_input.request_id}
        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

@ -152,7 +161,9 @@ async def async_request_trt_llm(
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
-            async with session.post(url=api_url, json=payload) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
@ -211,6 +222,8 @@ async def async_request_deepspeed_mii(
            "top_p": 1.0,
        }
        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
@ -283,6 +296,8 @@ async def async_request_openai_completions(
        if request_func_input.extra_body:
            payload.update(request_func_input.extra_body)
        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
@ -364,7 +379,15 @@ async def async_request_openai_chat_completions(
    ) as session:
        content = [{"type": "text", "text": request_func_input.prompt}]
        if request_func_input.multi_modal_content:
-            content.append(request_func_input.multi_modal_content)
+            mm_content = request_func_input.multi_modal_content
+            if isinstance(mm_content, list):
+                content.extend(mm_content)
+            elif isinstance(mm_content, dict):
+                content.append(mm_content)
+            else:
+                raise TypeError(
+                    "multi_modal_content must be a dict or list[dict] for openai-chat"
+                )
        payload = {
            "model": request_func_input.model_name
            if request_func_input.model_name
@ -387,6 +410,8 @@ async def async_request_openai_chat_completions(
            "Content-Type": "application/json",
            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
        }
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
@ -483,6 +508,8 @@ async def async_request_openai_audio(
        headers = {
            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
        }
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id

        # Send audio file
        def to_bytes(y, sr):
@ -491,7 +518,10 @@ async def async_request_openai_audio(
            buffer.seek(0)
            return buffer

-        with to_bytes(*request_func_input.multi_modal_content["audio"]) as f:
+        mm_audio = request_func_input.multi_modal_content
+        if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
+            raise TypeError("multi_modal_content must be a dict containing 'audio'")
+        with to_bytes(*mm_audio["audio"]) as f:
            form = aiohttp.FormData()
            form.add_field("file", f, content_type="audio/wav")
            for key, value in payload.items():
--- a/benchmarks/benchmark_block_pool.py
+++ b/benchmarks/benchmark_block_pool.py
@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
+
+from tabulate import tabulate
+
+from benchmark_utils import TimeCollector
+from vllm.utils import FlexibleArgumentParser
+from vllm.v1.core.block_pool import BlockPool
+
+
+def main(args):
+    rows = []
+    for allocate_block in args.allocate_blocks:
+        # Enforce a GC collect ahead to minimize the impact among runs
+        gc.collect()
+        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
+
+        get_blocks_times = TimeCollector(TimeCollector.US)
+        free_blocks_times = TimeCollector(TimeCollector.US)
+        for _ in range(args.num_iteration):
+            with get_blocks_times:
+                blocks = block_pool.get_new_blocks(allocate_block)
+            with free_blocks_times:
+                block_pool.free_blocks(blocks)
+
+        rows.append(
+            [get_blocks_times.cnt, args.num_gpu_blocks, allocate_block]
+            + get_blocks_times.dump_avg_max()
+            + free_blocks_times.dump_avg_max()
+        )
+
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "Iterations",
+                "Total\nBlocks",
+                "Allocated\nBlocks",
+                "Get Blocks\nAvg (us)",
+                "Get Blocks\nMax (us)",
+                "Free Blocks\nAvg (us)",
+                "Free Blocks\nMax (us)",
+            ],
+            tablefmt="grid",
+            floatfmt=".3f",
+        )
+    )
+
+
+def invoke_main() -> None:
+    parser = FlexibleArgumentParser(
+        description="Benchmark the performance of BlockPool for KV Cache."
+    )
+    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
+    parser.add_argument(
+        "--num-iteration",
+        type=int,
+        default=1000,
+        help="Number of iterations to run to stabilize final data readings",
+    )
+    parser.add_argument(
+        "--allocate-blocks",
+        type=int,
+        nargs="*",
+        default=[10, 50, 100, 500, 1000],
+        help="Number of blocks to allocate",
+    )
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    invoke_main()  # pragma: no cover
--- a/benchmarks/benchmark_dataset.py
+++ b/benchmarks/benchmark_dataset.py
@ -19,6 +19,7 @@ import logging
 import random
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
+from copy import deepcopy
 from dataclasses import dataclass
 from functools import cache
 from io import BytesIO
@ -52,8 +53,9 @@ class SampleRequest:
    prompt: Union[str, Any]
    prompt_len: int
    expected_output_len: int
-    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
+    multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
    lora_request: Optional[LoRARequest] = None
+    request_id: Optional[str] = None


 # -----------------------------------------------------------------------------
@ -155,7 +157,10 @@ class BenchmarkDataset(ABC):

    @abstractmethod
    def sample(
-        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        request_id_prefix: str = "",
    ) -> list[SampleRequest]:
        """
        Abstract method to generate sample requests from the dataset.
@ -167,6 +172,7 @@ class BenchmarkDataset(ABC):
            tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
             for processing the dataset's text.
            num_requests (int): The number of sample requests to generate.
+            request_id_prefix (str) The prefix of request_id.

        Returns:
            list[SampleRequest]: A list of sample requests generated from the
@ -175,7 +181,10 @@ class BenchmarkDataset(ABC):
        raise NotImplementedError("sample must be implemented in subclasses.")

    def maybe_oversample_requests(
-        self, requests: list[SampleRequest], num_requests: int
+        self,
+        requests: list[SampleRequest],
+        num_requests: int,
+        request_id_prefix: str = "",
    ) -> None:
        """
        Oversamples the list of requests if its size is less than the desired
@ -183,11 +192,18 @@ class BenchmarkDataset(ABC):

        Args:
            requests (List[SampleRequest]): The current list of sampled
-            requests.  num_requests (int): The target number of requests.
+            requests.
+            num_requests (int): The target number of requests.
+            request_id_prefix (str) The prefix of the request ids.
        """
        if len(requests) < num_requests:
            random.seed(self.random_seed)
-            additional = random.choices(requests, k=num_requests - len(requests))
+            additional = deepcopy(
+                random.choices(requests, k=num_requests - len(requests))
+            )
+            for i in range(len(additional)):
+                req = additional[i]
+                req.request_id = request_id_prefix + str(len(requests) + i)
            requests.extend(additional)
            logger.info("Oversampled requests to reach %d total samples.", num_requests)

@ -277,6 +293,41 @@ def process_image(image: Any) -> Mapping[str, Any]:
    )


+def process_video(video: Any) -> Mapping[str, Any]:
+    """
+    Process a single video input and return a multimedia content dictionary.
+
+    Supports the following input types:
+
+    1. Dictionary with raw video bytes: - Expects a dict with a 'bytes' key
+       containing raw video data.
+
+    2. String input: - Treats the string as a URL or local file path.  -
+       Prepends "file://" if the string doesn't start with "http://" or
+       "file://".  - Returns a dictionary with the image URL.
+
+    Raises:
+        ValueError: If the input is not a supported type.
+    """
+    if isinstance(video, dict) and "bytes" in video:
+        video_bytes = video["bytes"]
+        video_base64 = base64.b64encode(video_bytes).decode("utf-8")
+        return {
+            "type": "video_url",
+            "video_url": {"url": f"data:video/mp4;base64,{video_base64}"},
+        }
+
+    if isinstance(video, str):
+        video_url = (
+            video if video.startswith(("http://", "file://")) else f"file://{video}"
+        )
+        return {"type": "video_url", "video_url": {"url": video_url}}
+
+    raise ValueError(
+        f"Invalid video input {video}. Must be a string of local path/remote url, or a dictionary with raw video bytes in the form of `{{'bytes': raw_video_bytes}}`."  # noqa: E501
+    )
+
+
 # -----------------------------------------------------------------------------
 # Random Dataset Implementation (Synthetic Data)
 # -----------------------------------------------------------------------------
@ -303,6 +354,7 @@ class RandomDataset(BenchmarkDataset):
        range_ratio: float = DEFAULT_RANGE_RATIO,
        input_len: int = DEFAULT_INPUT_LEN,
        output_len: int = DEFAULT_OUTPUT_LEN,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list[SampleRequest]:
        # Enforce range_ratio < 1
@ -351,7 +403,7 @@ class RandomDataset(BenchmarkDataset):
            # [6880, 6881] -> ['Ġcalls', 'here'] ->
            # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
            # To avoid uncontrolled change of the prompt length,
-            # the encoded sequence is truncated before being decode again.
+            # the encoded sequence is truncated before being decoded again.
            total_input_len = prefix_len + int(input_lens[i])
            re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
                :total_input_len
@ -363,8 +415,10 @@ class RandomDataset(BenchmarkDataset):
                    prompt=prompt,
                    prompt_len=total_input_len,
                    expected_output_len=int(output_lens[i]),
+                    request_id=request_id_prefix + str(i),
                )
            )
+
        return requests


@ -406,9 +460,11 @@ class ShareGPTDataset(BenchmarkDataset):
        max_loras: Optional[int] = None,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        samples: list = []
+        ind = 0
        for entry in self.data:
            if len(samples) >= num_requests:
                break
@ -430,17 +486,26 @@ class ShareGPTDataset(BenchmarkDataset):
                skip_min_output_len_check=output_len is not None,
            ):
                continue
+            if image_path := entry.get("image"):
+                mm_content = process_image(image_path)
+            elif video_path := entry.get("video"):
+                mm_content = process_video(video_path)
+            else:
+                mm_content = None
            if enable_multimodal_chat:
-                prompt = self.apply_multimodal_chat_transformation(prompt, None)
+                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
            samples.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=new_output_len,
                    lora_request=lora_request,
+                    multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                )
            )
-        self.maybe_oversample_requests(samples, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
        return samples


@ -506,10 +571,11 @@ class CustomDataset(BenchmarkDataset):
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        skip_chat_template: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
            if len(sampled_requests) >= num_requests:
                break
            prompt = item["prompt"]
@ -528,9 +594,12 @@ class CustomDataset(BenchmarkDataset):
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )

        return sampled_requests

@ -572,6 +641,7 @@ class SonnetDataset(BenchmarkDataset):
        input_len: int = DEFAULT_INPUT_LEN,
        output_len: int = DEFAULT_OUTPUT_LEN,
        return_prompt_formatted: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        # Calculate average token length for a poem line.
@ -597,6 +667,7 @@ class SonnetDataset(BenchmarkDataset):
        prefix_lines = self.data[:num_prefix_lines]

        samples = []
+        ind = 0
        while len(samples) < num_requests:
            extra_lines = random.choices(
                self.data, k=num_input_lines - num_prefix_lines
@ -607,14 +678,17 @@ class SonnetDataset(BenchmarkDataset):
                msg, add_generation_prompt=True, tokenize=False
            )
            prompt_len = len(tokenizer(prompt_formatted).input_ids)
+
            if prompt_len <= input_len:
                samples.append(
                    SampleRequest(
                        prompt=prompt_formatted if return_prompt_formatted else prompt,
                        prompt_len=prompt_len,
                        expected_output_len=output_len,
+                        request_id=request_id_prefix + str(ind),
                    )
                )
+                ind += 1
        return samples


@ -666,6 +740,7 @@ class BurstGPTDataset(BenchmarkDataset):
        num_requests: int,
        max_loras: Optional[int] = None,
        lora_path: Optional[str] = None,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list[SampleRequest]:
        samples = []
@ -687,6 +762,7 @@ class BurstGPTDataset(BenchmarkDataset):
                    prompt_len=input_len,
                    expected_output_len=output_len,
                    lora_request=lora_req,
+                    request_id=request_id_prefix + str(i),
                )
            )
        return samples
@ -746,12 +822,14 @@ class ConversationDataset(HuggingFaceDataset):
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        # Filter examples with at least 2 conversations
        filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
        sampled_requests = []
        dynamic_output = output_len is None
+        ind = 0

        for item in filtered_data:
            if len(sampled_requests) >= num_requests:
@ -779,9 +857,13 @@ class ConversationDataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests


@ -808,11 +890,12 @@ class VisionArenaDataset(HuggingFaceDataset):
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
            if len(sampled_requests) >= num_requests:
                break
            parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
@ -832,9 +915,12 @@ class VisionArenaDataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(i),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests


@ -864,15 +950,18 @@ class InstructCoderDataset(HuggingFaceDataset):
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
            if len(sampled_requests) >= num_requests:
                break
-            prompt = f"{item['input']}\n\n{item['instruction']} Just output \
-            the code, do not include any explanation."
+            prompt = (
+                f"{item['input']}\n\n{item['instruction']} Just output "
+                "the code, do not include any explanation."
+            )

            # apply template
            prompt = tokenizer.apply_chat_template(
@ -886,9 +975,12 @@ class InstructCoderDataset(HuggingFaceDataset):
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests


@ -918,12 +1010,13 @@ class MTBenchDataset(HuggingFaceDataset):
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []

-        for item in self.data:
+        for i, item in enumerate(self.data):
            if len(sampled_requests) >= num_requests:
                break
            prompt = item["turns"][0]
@ -941,9 +1034,12 @@ class MTBenchDataset(HuggingFaceDataset):
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests


@ -968,10 +1064,12 @@ class AIMODataset(HuggingFaceDataset):
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        sampled_requests = []
        dynamic_output = output_len is None
+        ind = 0

        for item in self.data:
            if len(sampled_requests) >= num_requests:
@ -994,9 +1092,13 @@ class AIMODataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=None,
+                    request_id=request_id_prefix + str(ind),
                )
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests


@ -1066,12 +1168,18 @@ class NextEditPredictionDataset(HuggingFaceDataset):
        "zed-industries/zeta": _format_zeta_prompt,
    }

-    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        request_id_prefix: str = "",
+        **kwargs,
+    ):
        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
        if formatting_prompt_func is None:
            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
        samples = []
-        for sample in self.data:
+        for i, sample in enumerate(self.data):
            sample = formatting_prompt_func(sample)
            samples.append(
                SampleRequest(
@ -1080,11 +1188,12 @@ class NextEditPredictionDataset(HuggingFaceDataset):
                    expected_output_len=len(
                        tokenizer(sample["expected_output"]).input_ids
                    ),
+                    request_id=request_id_prefix + str(i),
                )
            )
            if len(samples) >= num_requests:
                break
-        self.maybe_oversample_requests(samples, num_requests)
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
        return samples


@ -1133,6 +1242,7 @@ class ASRDataset(HuggingFaceDataset):
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
+        request_id_prefix: str = "",
        **kwargs,
    ) -> list:
        import librosa
@ -1142,6 +1252,7 @@ class ASRDataset(HuggingFaceDataset):
        prompt_len = len(tokenizer(prompt).input_ids)
        sampled_requests = []
        skipped = 0
+        ind = 0
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
@ -1160,8 +1271,10 @@ class ASRDataset(HuggingFaceDataset):
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                )
            )
+            ind += 1
        if skipped:
            logger.warning(
                "%d samples discarded from dataset due to"
@ -1169,5 +1282,7 @@ class ASRDataset(HuggingFaceDataset):
                " what Whisper supports.",
                skipped,
            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
        return sampled_requests
--- a/benchmarks/benchmark_ngram_proposer.py
+++ b/benchmarks/benchmark_ngram_proposer.py
@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
+
+import numpy as np
+from tabulate import tabulate
+
+from benchmark_utils import TimeCollector
+from vllm.config import ModelConfig, SpeculativeConfig, VllmConfig
+from vllm.utils import FlexibleArgumentParser
+from vllm.v1.spec_decode.ngram_proposer import NgramProposer
+
+
+def main(args):
+    rows = []
+    for max_ngram in args.max_ngram:
+        collector = TimeCollector(TimeCollector.US)
+
+        model_config = ModelConfig(
+            model="facebook/opt-125m",
+            task="generate",
+            max_model_len=args.num_token + args.num_spec_token,
+            tokenizer="facebook/opt-125m",
+            tokenizer_mode="auto",
+            dtype="auto",
+            seed=None,
+            trust_remote_code=False,
+        )
+        proposer = NgramProposer(
+            vllm_config=VllmConfig(
+                model_config=model_config,
+                speculative_config=SpeculativeConfig(
+                    prompt_lookup_min=args.min_ngram,
+                    prompt_lookup_max=max_ngram,
+                    num_speculative_tokens=args.num_spec_token,
+                    method="ngram",
+                ),
+            )
+        )
+
+        # Warm up
+        proposer.propose(np.random.randint(0, 20, (args.num_token,)))
+
+        gc.collect()
+        for _ in range(args.num_iteration):
+            tokens = np.random.randint(0, 20, (args.num_req, args.num_token))
+            with collector:
+                for i in range(args.num_req):
+                    proposer.propose(tokens[i, :])
+        rows.append(
+            [args.num_req, args.num_token, args.min_ngram, max_ngram]
+            + collector.dump_avg_max()
+        )
+
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "# Request",
+                "# Token",
+                "Min Ngram",
+                "Max Ngram",
+                "Avg (us)",
+                "Max (us)",
+            ],
+            tablefmt="grid",
+            floatfmt=".3f",
+        )
+    )
+
+
+def invoke_main() -> None:
+    parser = FlexibleArgumentParser(
+        description="Benchmark the performance of N-gram speculative decode drafting"
+    )
+    parser.add_argument(
+        "--num-iteration",
+        type=int,
+        default=100,
+        help="Number of iterations to run to stabilize final data readings",
+    )
+    parser.add_argument(
+        "--num-req", type=int, default=128, help="Number of requests in the batch"
+    )
+    parser.add_argument(
+        "--num-token", type=int, default=1500, help="Number of tokens for each request"
+    )
+    parser.add_argument(
+        "--min-ngram",
+        type=int,
+        default=3,
+        help="Minimum n-gram to match",
+    )
+    parser.add_argument(
+        "--max-ngram",
+        type=int,
+        nargs="*",
+        default=[5, 7, 10, 15, 20],
+        help="Maximum n-gram to match",
+    )
+    parser.add_argument(
+        "--num-spec-token",
+        type=int,
+        default=3,
+        help="Number of speculative tokens to generate",
+    )
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    invoke_main()  # pragma: no cover
--- a/benchmarks/benchmark_serving.py
+++ b/benchmarks/benchmark_serving.py
@ -5,8 +5,7 @@ r"""Benchmark online serving throughput.
 On the server side, run one of the following commands:
    vLLM OpenAI API server
    vllm serve <your_model> \
-        --swap-space 16 \
-        --disable-log-requests
+        --swap-space 16

 On the client side, run:
    python benchmarks/benchmark_serving.py \
@ -264,7 +263,14 @@ async def benchmark(
        input_requests[0].multi_modal_data,
    )

-    assert test_mm_content is None or isinstance(test_mm_content, dict)
+    assert (
+        test_mm_content is None
+        or isinstance(test_mm_content, dict)
+        or (
+            isinstance(test_mm_content, list)
+            and all(isinstance(item, dict) for item in test_mm_content)
+        )
+    ), "multi_modal_data must be a dict or list[dict]"
    test_input = RequestFuncInput(
        model=model_id,
        model_name=model_name,
@ -369,11 +375,12 @@ async def benchmark(
                    rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
                last_int_rps = current_int_rps

-        prompt, prompt_len, output_len, mm_content = (
+        prompt, prompt_len, output_len, mm_content, request_id = (
            request.prompt,
            request.prompt_len,
            request.expected_output_len,
            request.multi_modal_data,
+            request.request_id,
        )
        req_model_id, req_model_name = model_id, model_name
        if lora_modules:
@ -391,6 +398,7 @@ async def benchmark(
            multi_modal_content=mm_content,
            ignore_eos=ignore_eos,
            extra_body=extra_body,
+            request_id=request_id,
        )
        task = limited_request_func(request_func_input=request_func_input, pbar=pbar)
        tasks.append(asyncio.create_task(task))
@ -659,6 +667,7 @@ def main(args: argparse.Namespace):
            tokenizer=tokenizer,
            output_len=args.custom_output_len,
            skip_chat_template=args.custom_skip_chat_template,
+            request_id_prefix=args.request_id_prefix,
        )

    elif args.dataset_name == "sonnet":
@ -672,6 +681,7 @@ def main(args: argparse.Namespace):
                prefix_len=args.sonnet_prefix_len,
                tokenizer=tokenizer,
                return_prompt_formatted=False,
+                request_id_prefix=args.request_id_prefix,
            )
        else:
            assert tokenizer.chat_template or tokenizer.default_chat_template, (
@ -684,6 +694,7 @@ def main(args: argparse.Namespace):
                prefix_len=args.sonnet_prefix_len,
                tokenizer=tokenizer,
                return_prompt_formatted=True,
+                request_id_prefix=args.request_id_prefix,
            )

    elif args.dataset_name == "hf":
@ -745,6 +756,7 @@ def main(args: argparse.Namespace):
            num_requests=args.num_prompts,
            tokenizer=tokenizer,
            output_len=args.hf_output_len,
+            request_id_prefix=args.request_id_prefix,
        )

    else:
@ -756,10 +768,15 @@ def main(args: argparse.Namespace):
                tokenizer=tokenizer,
                num_requests=args.num_prompts,
                output_len=args.sharegpt_output_len,
+                request_id_prefix=args.request_id_prefix,
            ),
            "burstgpt": lambda: BurstGPTDataset(
                random_seed=args.seed, dataset_path=args.dataset_path
-            ).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+            ).sample(
+                tokenizer=tokenizer,
+                num_requests=args.num_prompts,
+                request_id_prefix=args.request_id_prefix,
+            ),
            "random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
                tokenizer=tokenizer,
                num_requests=args.num_prompts,
@ -767,6 +784,7 @@ def main(args: argparse.Namespace):
                input_len=args.random_input_len,
                output_len=args.random_output_len,
                range_ratio=args.random_range_ratio,
+                request_id_prefix=args.request_id_prefix,
            ),
        }

@ -1086,7 +1104,7 @@ def create_argument_parser():
        "--percentile-metrics",
        type=str,
        default="ttft,tpot,itl",
-        help="Comma-separated list of selected metrics to report percentils. "
+        help="Comma-separated list of selected metrics to report percentiles. "
        "This argument specifies the metrics to report percentiles. "
        'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
        'Default value is "ttft,tpot,itl".',
@ -1112,6 +1130,13 @@ def create_argument_parser():
        "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
        "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
    )
+    parser.add_argument(
+        "--request-id-prefix",
+        type=str,
+        required=False,
+        default="benchmark-serving",
+        help="Specify the prefix of request id.",
+    )

    # group for dataset specific arguments
    custom_group = parser.add_argument_group("custom dataset options")
--- a/benchmarks/benchmark_serving_structured_output.py
+++ b/benchmarks/benchmark_serving_structured_output.py
@ -4,7 +4,7 @@ r"""Benchmark online serving throughput with structured outputs.

 On the server side, run one of the following commands:
    (vLLM OpenAI API server)
-    vllm serve <your_model> --disable-log-requests
+    vllm serve <your_model>

 On the client side, run:
    python benchmarks/benchmark_serving_structured_output.py \
@ -998,7 +998,7 @@ def create_argument_parser():
        "--percentile-metrics",
        type=str,
        default="ttft,tpot,itl",
-        help="Comma-separated list of selected metrics to report percentils. "
+        help="Comma-separated list of selected metrics to report percentiles. "
        "This argument specifies the metrics to report percentiles. "
        'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
        'Default value is "ttft,tpot,itl".',
--- a/benchmarks/benchmark_throughput.py
+++ b/benchmarks/benchmark_throughput.py
@ -96,7 +96,6 @@ def run_vllm(
        end = time.perf_counter()
    else:
        assert lora_requests is None, "BeamSearch API does not support LoRA"
-        prompts = [request.prompt for request in requests]
        # output_len should be the same for all requests.
        output_len = requests[0].expected_output_len
        for request in requests:
@ -597,8 +596,8 @@ def validate_args(args):
    # https://github.com/vllm-project/vllm/issues/16222
    if args.data_parallel_size > 1:
        raise ValueError(
-            "Data parallel is not supported in offline benchmark, \
-            please use benchmark serving instead"
+            "Data parallel is not supported in offline benchmark, "
+            "please use benchmark serving instead"
        )


@ -720,7 +719,7 @@ def create_argument_parser():
        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
    )

-    # hf dtaset
+    # hf dataset
    parser.add_argument(
        "--hf-subset", type=str, default=None, help="Subset of the HF dataset."
    )
--- a/benchmarks/benchmark_utils.py
+++ b/benchmarks/benchmark_utils.py
@ -1,11 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
 import argparse
 import json
 import math
 import os
-from typing import Any
+import time
+from types import TracebackType
+from typing import Any, Optional, Union


 def convert_to_pytorch_benchmark_format(
@ -72,3 +73,53 @@ def write_to_json(filename: str, records: list) -> None:
            cls=InfEncoder,
            default=lambda o: f"<{type(o).__name__} object is not JSON serializable>",
        )
+
+
+# Collect time and generate time metrics
+#
+# Example Usage:
+#   collector = TimeCollector(TimeCollector.US)
+#   for _ in range(total_iteration):
+#      with collector:
+#          ...
+#   collector.dump_avg_max()
+class TimeCollector:
+    NS: int = 1
+    US: int = NS * 1000
+    MS: int = US * 1000
+    S: int = MS * 1000
+
+    def __init__(self, scale: int) -> None:
+        self.cnt: int = 0
+        self._sum: int = 0
+        self._max: Optional[int] = None
+        self.scale = scale
+        self.start_time: int = time.monotonic_ns()
+
+    def collect(self, v: int) -> None:
+        self.cnt += 1
+        self._sum += v
+        if self._max is None:
+            self._max = v
+        else:
+            self._max = max(self._max, v)
+
+    def avg(self) -> Union[float, str]:
+        return self._sum * 1.0 / self.cnt / self.scale if self.cnt > 0 else "N/A"
+
+    def max(self) -> Union[float, str]:
+        return self._max / self.scale if self._max else "N/A"
+
+    def dump_avg_max(self) -> list[Union[float, str]]:
+        return [self.avg(), self.max()]
+
+    def __enter__(self) -> None:
+        self.start_time = time.monotonic_ns()
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        exc_traceback: Optional[TracebackType],
+    ) -> None:
+        self.collect(time.monotonic_ns() - self.start_time)
--- a/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh
+++ b/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh
@ -62,7 +62,7 @@ benchmark() {
    --max-model-len 10000 \
    --gpu-memory-utilization 0.6 \
    --kv-transfer-config \
-    '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
+    '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &


  CUDA_VISIBLE_DEVICES=1 python3 \
@ -72,7 +72,7 @@ benchmark() {
    --max-model-len 10000 \
    --gpu-memory-utilization 0.6 \
    --kv-transfer-config \
-    '{"kv_connector":"PyNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
+    '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2,"kv_buffer_size":5e9}' &

  wait_for_server 8100
  wait_for_server 8200
--- a/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh
+++ b/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh
@ -69,7 +69,7 @@ launch_disagg_prefill() {
    --max-model-len 10000 \
    --gpu-memory-utilization 0.6 \
    --kv-transfer-config \
-    '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
+    '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &

  CUDA_VISIBLE_DEVICES=1 python3 \
    -m vllm.entrypoints.openai.api_server \
@ -78,7 +78,7 @@ launch_disagg_prefill() {
    --max-model-len 10000 \
    --gpu-memory-utilization 0.6 \
    --kv-transfer-config \
-    '{"kv_connector":"PyNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
+    '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2,"kv_buffer_size":5e9}' &

  wait_for_server 8100
  wait_for_server 8200
--- a/benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py
+++ b/benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py
@ -1,63 +1,199 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

+import argparse
+import asyncio
+import logging
 import os

 import aiohttp
-from quart import Quart, make_response, request
+from quart import Quart, Response, make_response, request
+from rate_limiter import RateLimiter
+from request_queue import RequestQueue

-AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
-
-app = Quart(__name__)
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)


-async def forward_request(url, data):
-    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
+def parse_args():
+    """parse command line arguments"""
+    parser = argparse.ArgumentParser(description="vLLM P/D disaggregation proxy server")
+
+    # Add args
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=300,
+        help="Timeout for backend service requests in seconds (default: 300)",
+    )
+    parser.add_argument(
+        "--max-concurrent",
+        type=int,
+        default=100,
+        help="Maximum concurrent requests to backend services (default: 100)",
+    )
+    parser.add_argument(
+        "--queue-size",
+        type=int,
+        default=500,
+        help="Maximum number of requests in the queue (default: 500)",
+    )
+    parser.add_argument(
+        "--rate-limit",
+        type=int,
+        default=40,
+        help="Maximum requests per second (default: 40)",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="Port to run the server on (default: 8000)",
+    )
+    parser.add_argument(
+        "--prefill-url",
+        type=str,
+        default="http://localhost:8100/v1/completions",
+        help="Prefill service endpoint URL",
+    )
+    parser.add_argument(
+        "--decode-url",
+        type=str,
+        default="http://localhost:8200/v1/completions",
+        help="Decode service endpoint URL",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    """parse command line arguments"""
+    args = parse_args()
+
+    # Initialize configuration using command line parameters
+    AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=args.timeout)
+    MAX_CONCURRENT_REQUESTS = args.max_concurrent
+    REQUEST_QUEUE_SIZE = args.queue_size
+    RATE_LIMIT = args.rate_limit
+    PREFILL_SERVICE_URL = args.prefill_url
+    DECODE_SERVICE_URL = args.decode_url
+    PORT = args.port
+
+    app = Quart(__name__)
+
+    # Initialize the rate limiter and request queue
+    rate_limiter = RateLimiter(RATE_LIMIT)
+    request_queue = RequestQueue(MAX_CONCURRENT_REQUESTS, REQUEST_QUEUE_SIZE)
+
+    # Attach the configuration object to the application instance
+    app.config.update(
+        {
+            "AIOHTTP_TIMEOUT": AIOHTTP_TIMEOUT,
+            "rate_limiter": rate_limiter,
+            "request_queue": request_queue,
+            "PREFILL_SERVICE_URL": PREFILL_SERVICE_URL,
+            "DECODE_SERVICE_URL": DECODE_SERVICE_URL,
+        }
+    )
+
+    # Start queue processing on app startup
+    @app.before_serving
+    async def startup():
+        """Start request processing task when app starts serving"""
+        asyncio.create_task(request_queue.process())
+
+    async def forward_request(url, data):
+        """Forward request to backend service with rate limiting and error handling"""
        headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
-        async with session.post(url=url, json=data, headers=headers) as response:
-            if response.status == 200:
-                # if response.headers.get('Transfer-Encoding') == 'chunked':
-                if True:
-                    async for chunk_bytes in response.content.iter_chunked(1024):
-                        yield chunk_bytes
-                else:
-                    content = await response.read()
-                    yield content

-
-@app.route("/v1/completions", methods=["POST"])
-async def handle_request():
-    try:
-        original_request_data = await request.get_json()
-
-        prefill_request = original_request_data.copy()
-        # change max_tokens = 1 to let it only do prefill
-        prefill_request["max_tokens"] = 1
-
-        # finish prefill
-        async for _ in forward_request(
-            "http://localhost:8100/v1/completions", prefill_request
+        # Use rate limiter as context manager
+        async with (
+            rate_limiter,
+            aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session,
        ):
-            continue
+            try:
+                async with session.post(
+                    url=url, json=data, headers=headers
+                ) as response:
+                    if response.status == 200:
+                        # Stream response chunks
+                        async for chunk_bytes in response.content.iter_chunked(1024):
+                            yield chunk_bytes
+                    else:
+                        # Handle backend service errors
+                        error_text = await response.text()
+                        logger.error(
+                            "Backend service error: %s - %s",
+                            response.status,
+                            error_text,
+                        )
+                        yield b'{"error": "Backend service error"}'
+            except aiohttp.ClientError as e:
+                # Handle connection errors
+                logger.error("Connection error to %s: %s", url, str(e))
+                yield b'{"error": "Service unavailable"}'
+            except asyncio.TimeoutError:
+                # Handle timeout errors
+                logger.error("Timeout connecting to %s", url)
+                yield b'{"error": "Service timeout"}'

-        # return decode
-        generator = forward_request(
-            "http://localhost:8200/v1/completions", original_request_data
-        )
-        response = await make_response(generator)
-        response.timeout = None
+    async def process_request():
+        """Process a single request through prefill and decode stages"""
+        try:
+            original_request_data = await request.get_json()

-        return response
+            # Create prefill request (max_tokens=1)
+            prefill_request = original_request_data.copy()
+            prefill_request["max_tokens"] = 1

-    except Exception as e:
-        import sys
-        import traceback
+            # Execute prefill stage
+            async for _ in forward_request(PREFILL_SERVICE_URL, prefill_request):
+                continue

-        exc_info = sys.exc_info()
-        print("Error occurred in disagg prefill proxy server")
-        print(e)
-        print("".join(traceback.format_exception(*exc_info)))
+            # Execute decode stage and stream response
+            generator = forward_request(DECODE_SERVICE_URL, original_request_data)
+            response = await make_response(generator)
+            response.timeout = None  # Disable timeout for streaming response
+            return response
+
+        except Exception:
+            logger.exception("Error processing request")
+            return Response(
+                response=b'{"error": "Internal server error"}',
+                status=500,
+                content_type="application/json",
+            )
+
+    @app.route("/v1/completions", methods=["POST"])
+    async def handle_request():
+        """Handle incoming API requests with concurrency and rate limiting"""
+        # Create task for request processing
+        task = asyncio.create_task(process_request())
+
+        # Enqueue request or reject if queue is full
+        if not await request_queue.enqueue(task):
+            return Response(
+                response=b'{"error": "Server busy, try again later"}',
+                status=503,
+                content_type="application/json",
+            )
+
+        try:
+            # Return the response from the processing task
+            return await task
+        except asyncio.CancelledError:
+            # Handle task cancellation (timeout or queue full)
+            logger.warning("Request cancelled due to timeout or queue full")
+            return Response(
+                response=b'{"error": "Request cancelled"}',
+                status=503,
+                content_type="application/json",
+            )
+
+    # Start the Quart server with host can be set to 0.0.0.0
+    app.run(port=PORT)


 if __name__ == "__main__":
-    app.run(port=8000)
+    main()
--- a/benchmarks/disagg_benchmarks/rate_limiter.py
+++ b/benchmarks/disagg_benchmarks/rate_limiter.py
@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import time
+
+
+class RateLimiter:
+    """Token bucket rate limiter implementation"""
+
+    def __init__(self, rate_limit):
+        self.rate_limit = rate_limit  # Requests per second
+        self.num_available_tokens = rate_limit  # Available tokens
+        self.last_refill = time.monotonic()  # Last token refill time
+        self.lock = asyncio.Lock()  # Synchronization lock
+
+    async def acquire(self):
+        """Acquire a token from the rate limiter"""
+        while True:
+            async with self.lock:
+                current_time = time.monotonic()
+                elapsed = current_time - self.last_refill
+
+                # Refill num_available_tokens if more than 1 second has passed
+                if elapsed > 1.0:
+                    self.num_available_tokens = self.rate_limit
+                    self.last_refill = current_time
+
+                # Check if num_available_tokens are available
+                if self.num_available_tokens > 0:
+                    self.num_available_tokens -= 1
+                    return True
+
+                # Calculate wait time if no num_available_tokens available
+                wait_time = 1.0 - elapsed
+            await asyncio.sleep(wait_time)
+
+    async def __aenter__(self):
+        """Enter async context manager - acquire token"""
+        await self.acquire()
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback):
+        """Exit async context manager - no cleanup needed"""
+        pass
--- a/benchmarks/disagg_benchmarks/request_queue.py
+++ b/benchmarks/disagg_benchmarks/request_queue.py
@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+from collections import deque
+
+
+class RequestQueue:
+    """Request queue manager with concurrency control"""
+
+    def __init__(self, max_concurrent, max_queue_size):
+        # Maximum concurrent requests
+        self.max_concurrent = max_concurrent
+        self.max_queue_size = max_queue_size  # Maximum queue size
+        # Concurrency control
+        self.semaphore = asyncio.Semaphore(max_concurrent)
+        self.queue = deque()  # Request queue
+        self.queue_size = 0  # Current queue size
+        self.lock = asyncio.Lock()  # Sync queue Lock
+
+    async def enqueue(self, task):
+        """Add a request task to the queue"""
+        async with self.lock:
+            if self.queue_size >= self.max_queue_size:
+                return False
+
+            self.queue.append(task)
+            self.queue_size += 1
+            return True
+
+    async def process(self):
+        """Process queued requests using semaphore for concurrency control"""
+        while True:
+            if self.queue:
+                async with self.semaphore, self.lock:
+                    task = self.queue.popleft()
+                    self.queue_size -= 1
+                    await task
+            await asyncio.sleep(0.01)  # Yield control to event loop
--- a/benchmarks/kernels/bench_block_fp8_gemm.py
+++ b/benchmarks/kernels/bench_block_fp8_gemm.py
@ -0,0 +1,114 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    w8a8_block_fp8_matmul,
+)
+from vllm.platforms import current_platform
+from vllm.triton_utils import triton as vllm_triton
+
+assert current_platform.is_cuda(), (
+    "Only support benchmarking w8a8 block fp8 kernel on CUDA device."
+)
+
+# DeepSeek-V3 weight shapes
+DEEPSEEK_V3_SHAPES = [
+    (512 + 64, 7168),
+    (2112, 7168),
+    ((128 + 64) * 128, 7168),
+    (128 * (128 + 128), 512),
+    (7168, 16384),
+    (7168, 18432),
+    (18432 * 2, 7168),
+    (24576, 1536),
+    (12288, 7168),
+    (4096, 7168),
+    (7168, 2048),
+]
+
+
+def build_w8a8_block_fp8_runner(M, N, K, block_size, device):
+    """Build runner function for w8a8 block fp8 matmul."""
+    factor_for_scale = 1e-2
+
+    fp8_info = torch.finfo(torch.float8_e4m3fn)
+    fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+    # Create random FP8 tensors
+    A_fp32 = (torch.rand(M, K, dtype=torch.float32, device=device) - 0.5) * 2 * fp8_max
+    A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+    B_fp32 = (torch.rand(N, K, dtype=torch.float32, device=device) - 0.5) * 2 * fp8_max
+    B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+    # Create scales
+    block_n, block_k = block_size[0], block_size[1]
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+
+    As = torch.rand(M, k_tiles, dtype=torch.float32, device=device) * factor_for_scale
+    Bs = (
+        torch.rand(n_tiles, k_tiles, dtype=torch.float32, device=device)
+        * factor_for_scale
+    )
+
+    def run():
+        return w8a8_block_fp8_matmul(A, B, As, Bs, block_size, torch.bfloat16)
+
+    return run
+
+
+@vllm_triton.testing.perf_report(
+    vllm_triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
+        x_log=False,
+        line_arg="provider",
+        line_vals=["torch-bf16", "w8a8-block-fp8"],
+        line_names=["torch-bf16", "w8a8-block-fp8"],
+        ylabel="TFLOP/s (larger is better)",
+        plot_name="BF16 vs W8A8 Block FP8 GEMMs",
+        args={},
+    )
+)
+def benchmark_tflops(batch_size, provider, N, K, block_size=(128, 128)):
+    M = batch_size
+    device = "cuda"
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "torch-bf16":
+        a = torch.randn((M, K), device=device, dtype=torch.bfloat16)
+        b = torch.randn((N, K), device=device, dtype=torch.bfloat16)
+        ms, min_ms, max_ms = vllm_triton.testing.do_bench_cudagraph(
+            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
+        )
+    else:  # w8a8-block-fp8
+        run_w8a8 = build_w8a8_block_fp8_runner(M, N, K, block_size, device)
+        ms, min_ms, max_ms = vllm_triton.testing.do_bench_cudagraph(
+            lambda: run_w8a8(), quantiles=quantiles
+        )
+
+    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
+    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
+
+
+if __name__ == "__main__":
+    block_size = (128, 128)
+
+    for N, K in DEEPSEEK_V3_SHAPES:
+        print(f"\nBenchmarking DeepSeek-V3, N={N} K={K}")
+
+        print(f"TFLOP/s comparison (block_size={block_size}):")
+        benchmark_tflops.run(
+            print_data=True,
+            # show_plots=False,
+            # save_path=f"bench_w8a8_block_fp8_tflops_n{N}_k{K}",
+            N=N,
+            K=K,
+            block_size=block_size,
+        )
+
+    print("\nBenchmark finished!")
--- a/benchmarks/kernels/benchmark_activation.py
+++ b/benchmarks/kernels/benchmark_activation.py
@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# benchmark custom activation op performance
+import itertools
+
+import torch
+
+import vllm.model_executor.layers.activation  # noqa F401
+from vllm.model_executor.custom_op import CustomOp
+from vllm.platforms import current_platform
+from vllm.triton_utils import triton
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
+
+batch_size_range = [1, 16, 32, 64, 128]
+seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
+intermediate_size = [3072, 9728, 12288]
+configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
+
+
+def benchmark_activation(
+    batch_size: int,
+    seq_len: int,
+    intermediate_size: int,
+    provider: str,
+    func_name: str,
+    dtype: torch.dtype,
+):
+    device = "cuda"
+    num_tokens = batch_size * seq_len
+    dim = intermediate_size
+    current_platform.seed_everything(42)
+    torch.set_default_device(device)
+
+    if func_name == "gelu_and_mul":
+        layer = CustomOp.op_registry[func_name](approximate="none")
+    elif func_name == "gelu_and_mul_tanh":
+        layer = CustomOp.op_registry["gelu_and_mul"](approximate="tanh")
+    elif func_name == "fatrelu_and_mul":
+        threshold = 0.5
+        layer = CustomOp.op_registry[func_name](threshold)
+    else:
+        layer = CustomOp.op_registry[func_name]()
+
+    x = torch.randn(num_tokens, dim, dtype=dtype, device=device)
+    compiled_layer = torch.compile(layer.forward_native)
+
+    if provider == "custom":
+        fn = lambda: layer(x)
+    elif provider == "compiled":
+        fn = lambda: compiled_layer(x)
+
+    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+        fn, quantiles=[0.5, 0.2, 0.8]
+    )
+    return ms, max_ms, min_ms
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the custom activation op.")
+    parser.add_argument(
+        "--func-name",
+        type=str,
+        choices=[
+            "mul_and_silu",
+            "silu_and_mul",
+            "gelu_and_mul",
+            "gelu_and_mul_tanh",
+            "fatrelu_and_mul",
+            "swigluoai_and_mul",
+            "gelu_new",
+            "gelu_fast",
+            "quick_gelu",
+        ],
+        default="silu_and_mul",
+    )
+    parser.add_argument(
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="bfloat16"
+    )
+    args = parser.parse_args()
+    assert args
+
+    func_name = args.func_name
+    dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
+
+    perf_report = triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["batch_size", "seq_len", "intermediate_size"],
+            x_vals=configs,
+            line_arg="provider",
+            line_vals=["custom", "compiled"],
+            line_names=["Custom OP", "Compiled"],
+            styles=[("blue", "-"), ("green", "-")],
+            ylabel="ms",
+            plot_name=f"{func_name}-op-performance",
+            args={},
+        )
+    )
+
+    perf_report(
+        lambda batch_size, seq_len, intermediate_size, provider: benchmark_activation(
+            batch_size, seq_len, intermediate_size, provider, func_name, dtype
+        )
+    ).run(print_data=True)
--- a/benchmarks/kernels/benchmark_aqlm.py
+++ b/benchmarks/kernels/benchmark_aqlm.py
@ -1,345 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import os
-import sys
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.aqlm import (
-    dequantize_weight,
-    generic_dequantize_gemm,
-    get_int_dtype,
-    optimized_dequantize_gemm,
-)
-from vllm.utils import FlexibleArgumentParser
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-
-def torch_mult(
-    # [..., in_features]
-    input: torch.Tensor,
-    weights: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-) -> torch.Tensor:
-    output = F.linear(input, weights)
-    return output
-
-
-def dequant_out_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    if bias is None:
-        output = F.linear(input, weights, bias)
-        orig_shape = output.shape
-        flattened_output = output.view(-1, output.size(-1))
-        f_scales = scales.view(-1, scales.shape[0])
-        b_scales = f_scales.expand(flattened_output.shape[0], -1)
-        flattened_output *= b_scales
-        return flattened_output.view(orig_shape)
-    else:
-        b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
-        weights *= b_scales
-        return F.linear(input, weights, bias)
-
-
-def dequant_weight_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
-    weights *= b_scales
-    return F.linear(input, weights, bias)
-
-
-def dequant_no_scale(
-    # [..., in_features]
-    input: torch.Tensor,
-    # [num_out_groups, num_in_groups, num_codebooks]
-    codes: torch.IntTensor,
-    # [num_codebooks, codebook_size, out_group_size, in_group_size]
-    codebooks: torch.Tensor,
-    # [num_out_groups, 1, 1, 1]
-    scales: torch.Tensor,
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    return F.linear(input, weights, bias)
-
-
-# Compare the optimized 1x16 and 2x8 cuda decompression/dequant kernels against
-# the generic pytorch version.
-# Just visual comparison.
-def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:
-    n = int(parts.sum().item())
-
-    device = torch.device("cuda:0")
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(
-        -code_range,
-        code_range,
-        size=(n, k // ingroups, nbooks),
-        dtype=get_int_dtype(bits),
-        device=device,
-    )
-
-    codebooks = torch.randn(
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        dtype=torch.float16,
-        device=device,
-    )
-
-    count = 0
-    for index in range(16):
-        for i in range(8):
-            for book in range(nbooks):
-                codebooks[book, index, 0, i] = count * (10**book)
-            count += 1
-
-    print("codes shape", codes.shape)
-
-    for i in range(16):
-        for book in range(nbooks):
-            codes[0, i, book] = i
-            codes[0, -i, book] = i
-
-    weights = dequantize_weight(codes, codebooks, None)
-    weights2 = ops.aqlm_dequant(codes, codebooks, parts)
-
-    print("weights shape:", weights.shape)
-    print("weights2 shape:", weights2.shape)
-
-    print("weights are:", weights)
-    print("weights2 are:", weights2)
-
-    print("first 128 weights are", weights[0, 0:128].to(torch.int32))
-    print("first 128 weights2 are:", weights2[0, 0:128].to(torch.int32))
-
-    print("last 128 weights are", weights[0, -128:])
-    print("last 128 weights2 are:", weights2[0, -128:])
-
-
-def main():
-    parser = FlexibleArgumentParser(description="Benchmark aqlm performance.")
-
-    # Add arguments
-    parser.add_argument(
-        "--nbooks", type=int, default=1, help="Number of codebooks (default: 1)"
-    )
-    parser.add_argument(
-        "--bits",
-        type=int,
-        default=16,
-        help="Number of bits per code element (default: 16)",
-    )
-    parser.add_argument(
-        "--test",
-        type=bool,
-        default=False,
-        help="Run the decompression/dequant tester rather than benchmarking "
-        "(default: False)",
-    )
-
-    # Parse the arguments
-    args = parser.parse_args()
-
-    # Extract values
-    nbooks = args.nbooks
-    bits = args.bits
-
-    if args.test:
-        dequant_test(4096, torch.tensor((4096,)), nbooks, bits)
-        return
-
-    # Otherwise, benchmark.
-    methods = [
-        ops.aqlm_gemm,
-        dequant_out_scale,
-        generic_dequantize_gemm,
-        optimized_dequantize_gemm,
-        dequant_weight_scale,
-        torch_mult,
-        dequant_no_scale,
-    ]
-
-    filename = f"./aqlm_benchmark_{nbooks}x{bits}.csv"
-    print(f"writing benchmarks to file {filename}")
-    with open(filename, "w") as f:
-        sys.stdout = f
-
-        print("m | k | n | n parts", end="")
-        for method in methods:
-            print(f" | {method.__name__.replace('_', ' ')} (µs)", end="")
-        print("")
-
-        # These are reasonable prefill sizes.
-        ksandpartions = (
-            (4096, (4096, 4096, 4096)),
-            (4096, (4096,)),
-            (4096, (11008, 11008)),
-            (11008, (4096,)),
-        )
-
-        # reasonable ranges for m.
-        for m in [
-            1,
-            2,
-            4,
-            8,
-            10,
-            12,
-            14,
-            16,
-            24,
-            32,
-            48,
-            52,
-            56,
-            64,
-            96,
-            112,
-            128,
-            256,
-            512,
-            1024,
-            1536,
-            2048,
-            3072,
-            4096,
-        ]:
-            print(f"{m}", file=sys.__stdout__)
-            for ksp in ksandpartions:
-                run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits, methods)
-
-        sys.stdout = sys.__stdout__
-
-
-def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, methods):
-    # I didn't see visible improvements from increasing these, but feel free :)
-    num_warmup_trials = 1
-    num_trials = 1
-
-    num_calls = 100
-
-    # warmup.
-    for method in methods:
-        for _ in range(num_warmup_trials):
-            run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-    n = parts.sum().item()
-    print(f"{m} | {k} | {n} | {parts.tolist()}", end="")
-
-    for method in methods:
-        best_time_us = 1e20
-        for _ in range(num_trials):
-            kernel_dur_ms = run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-            kernel_dur_us = 1000 * kernel_dur_ms
-
-            if kernel_dur_us < best_time_us:
-                best_time_us = kernel_dur_us
-
-        print(f" | {kernel_dur_us:.0f}", end="")
-
-    print("")
-
-
-def run_timing(
-    num_calls: int, m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
-) -> float:
-    n = int(parts.sum().item())
-
-    device = torch.device("cuda:0")
-
-    input = torch.randn((1, m, k), dtype=torch.float16, device=device)
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(
-        -code_range,
-        code_range,
-        size=(n, k // ingroups, nbooks),
-        dtype=get_int_dtype(bits),
-        device=device,
-    )
-
-    codebooks = torch.randn(
-        size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-        dtype=torch.float16,
-        device=device,
-    )
-
-    scales = torch.randn(size=(n, 1, 1, 1), dtype=torch.float16, device=device)
-
-    # for comparison to just a pytorch mult.
-    weights = torch.randn((n, k), dtype=torch.float16, device=device)
-
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-
-    start_event.record()
-
-    if method is torch_mult:
-        for i in range(num_calls):
-            torch_mult(input, weights, scales)
-    else:
-        for i in range(num_calls):
-            method(input, codes, codebooks, scales, parts, None)
-
-    end_event.record()
-    end_event.synchronize()
-
-    dur_ms = start_event.elapsed_time(end_event) / num_calls
-    return dur_ms
-
-
-if __name__ == "__main__":
-    sys.exit(main())
--- a/benchmarks/kernels/benchmark_bitblas.py
+++ b/benchmarks/kernels/benchmark_bitblas.py
@ -3,6 +3,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.

+from packaging import version
+
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
    MINIMUM_BITBLAS_VERSION,
 )
@ -10,7 +12,7 @@ from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
 try:
    import bitblas

-    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+    if version.parse(bitblas.__version__) < version.parse(MINIMUM_BITBLAS_VERSION):
        raise ImportError(
            "bitblas version is wrong. Please "
            f"install bitblas>={MINIMUM_BITBLAS_VERSION}"
--- a/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
+++ b/benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
@ -80,6 +80,11 @@ def bench_run(
        a, score, topk, renormalize=False
    )

+    ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
+    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
+    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+
    def run_triton_moe(
        a: torch.Tensor,
        w1: torch.Tensor,
@ -111,6 +116,10 @@ def bench_run(
        w2: torch.Tensor,
        w1_scale: torch.Tensor,
        w2_scale: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        per_act_token: bool,
@ -125,6 +134,10 @@ def bench_run(
                topk_ids,
                w1_scale,
                w2_scale,
+                ab_strides1,
+                ab_strides2,
+                c_strides1,
+                c_strides2,
                per_act_token,
                a1_scale=None,
            )
@ -136,6 +149,10 @@ def bench_run(
        w2_q: torch.Tensor,
        w1_scale: torch.Tensor,
        w2_scale: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
    ):
@ -150,6 +167,10 @@ def bench_run(
                topk_ids,
                w1_scale,
                w2_scale,
+                ab_strides1,
+                ab_strides2,
+                c_strides1,
+                c_strides2,
                per_act_token,
                a1_scale=None,
            )
@ -194,6 +215,10 @@ def bench_run(
            w2_q,
            w1_scale,
            w2_scale,
+            ab_strides1,
+            ab_strides2,
+            c_strides1,
+            c_strides2,
            topk_weights,
            topk_ids,
        )
@ -231,6 +256,10 @@ def bench_run(
        "w1_scale": w1_scale,
        "w2_scale": w2_scale,
        "per_act_token": per_act_token,
+        "ab_strides1": ab_strides1,
+        "ab_strides2": ab_strides2,
+        "c_strides1": c_strides1,
+        "c_strides2": c_strides2,
        # cuda graph params
        "cutlass_graph": cutlass_graph,
        "triton_graph": triton_graph,
@ -289,6 +318,10 @@ def bench_run(
        w2_q,
        w1_scale,
        w2_scale,
+        ab_strides1,
+        ab_strides2,
+        c_strides1,
+        c_strides2,
        topk_weights,
        topk_ids,
        per_act_token,
@ -297,7 +330,7 @@ def bench_run(

    results.append(
        benchmark.Timer(
-            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, ab_strides1, ab_strides2, c_strides1, c_strides2, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
            globals=globals,
            label=label,
            sub_label=sub_label,
--- a/benchmarks/kernels/benchmark_lora.py
+++ b/benchmarks/kernels/benchmark_lora.py
@ -637,7 +637,7 @@ def bench_optype(
    # Clear LoRA optimization hash-maps.
    _LORA_A_PTR_DICT.clear()
    _LORA_B_PTR_DICT.clear()
-    # Run bench function so that _LORA_A_PTR_DICT and _LORA_B_PTR_DICT are setup
+    # Run bench function so that _LORA_A_PTR_DICT and _LORA_B_PTR_DICT are set up
    for kwargs in kwargs_list:
        op_type.bench_fn()(**kwargs)
    torch.cuda.synchronize()
--- a/benchmarks/kernels/benchmark_machete.py
+++ b/benchmarks/kernels/benchmark_machete.py
@ -236,6 +236,7 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
            a=bt.a,
            c=None,
            b_q_weight=w_q,
+            b_bias=None,
            b_scales=w_s,
            global_scale=None,
            b_zeros=w_zp,
@ -252,28 +253,7 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
    else:
        assert bt.a.dtype == torch.int8
        assert bt.wtype == scalar_types.uint4b8
-
-        if bt.w_ch_s is not None:
-            s_ch = bt.w_ch_s.to(torch.float32)
-        else:
-            s_ch = torch.ones(bt.w_ref.shape[1], dtype=torch.float32, device=device)
-
-        if bt.w_tok_s is not None:
-            s_tok = bt.w_tok_s.to(torch.float32)
-        else:
-            s_tok = torch.ones(bt.a.shape[0], dtype=torch.float32, device=device)
-
-        fn = lambda: ops.marlin_qqq_gemm(
-            a=bt.a,
-            b_q_weight=w_q,
-            s_group=w_s,
-            s_tok=s_tok,
-            s_ch=s_ch,
-            workspace=workspace.scratch,
-            size_m=bt.a.shape[0],
-            size_n=bt.w_ref.shape[1],
-            size_k=bt.w_ref.shape[0],
-        )
+        raise NotImplementedError("QQQ is not supported anymore")

    return fn

@ -304,6 +284,25 @@ def machete_create_bench_fn(
    )


+def cutlass_w4a8_create_bench_fn(
+    bt: BenchmarkTensors, out_type=torch.dtype, schedule=None
+) -> Callable:
+    w_q = bt.w_q.t().contiguous().t()  # make col major
+    w_q = ops.cutlass_encode_and_reorder_int4b(w_q)
+    # expects fp8 scales
+    w_s = ops.cutlass_pack_scale_fp8(bt.w_g_s.to(torch.float8_e4m3fn))
+
+    return lambda: ops.cutlass_w4a8_mm(
+        a=bt.a,
+        b_q=w_q,
+        b_group_scales=w_s,
+        b_group_size=bt.group_size,
+        b_channel_scales=bt.w_ch_s,
+        a_token_scales=bt.w_tok_s,
+        maybe_schedule=schedule,
+    )
+
+
 # impl

 # bench
@ -405,6 +404,20 @@ def bench(
        )
    )

+    # cutlass w4a8
+    if types.act_type == torch.float8_e4m3fn and group_size == 128:
+        timers.append(
+            bench_fns(
+                label,
+                sub_label,
+                f"cutlass w4a8 ({name_type_string})",
+                [
+                    cutlass_w4a8_create_bench_fn(bt, out_type=types.output_type)
+                    for bt in benchmark_tensors
+                ],
+            )
+        )
+
    if sweep_schedules:
        global _SWEEP_SCHEDULES_RESULTS

--- a/benchmarks/kernels/benchmark_moe.py
+++ b/benchmarks/kernels/benchmark_moe.py
@ -3,6 +3,7 @@

 import argparse
 import json
+import os
 import time
 from contextlib import nullcontext
 from datetime import datetime
@ -22,10 +23,10 @@ from vllm.utils import FlexibleArgumentParser
 FP8_DTYPE = current_platform.fp8_dtype()


-def ensure_divisibility(numerator, denominator):
+def ensure_divisibility(numerator, denominator, text):
    """Ensure that numerator is divisible by the denominator."""
-    assert numerator % denominator == 0, (
-        "intermediate_size {} is not divisible by tp {}.".format(numerator, denominator)
+    assert numerator % denominator == 0, "{} {} is not divisible by tp {}.".format(
+        text, numerator, denominator
    )


@ -418,8 +419,10 @@ class BenchmarkWorker:
        )
        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
        # is the intermediate size after silu_and_mul.
+        block_n = block_quant_shape[0] if block_quant_shape else None
+        block_k = block_quant_shape[1] if block_quant_shape else None
        op_config = get_moe_configs(
-            num_experts, shard_intermediate_size // 2, dtype_str
+            num_experts, shard_intermediate_size // 2, dtype_str, block_n, block_k
        )
        if op_config is None:
            config = get_default_config(
@ -429,7 +432,7 @@ class BenchmarkWorker:
                hidden_size,
                topk,
                dtype_str,
-                is_marlin=False,
+                block_quant_shape,
            )
        else:
            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
@ -542,6 +545,7 @@ def save_configs(
    use_fp8_w8a8: bool,
    use_int8_w8a16: bool,
    block_quant_shape: list[int],
+    save_dir: str,
 ) -> None:
    dtype_str = get_config_dtype_str(
        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
@ -552,7 +556,8 @@ def save_configs(
    filename = get_config_file_name(
        num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape
    )
-
+    os.makedirs(save_dir, exist_ok=True)
+    filename = os.path.join(save_dir, filename)
    print(f"Writing best config to {filename}...")
    with open(filename, "w") as f:
        json.dump(configs, f, indent=4)
@ -577,12 +582,10 @@ def main(args: argparse.Namespace):
        E = config.ffn_config.moe_num_experts
        topk = config.ffn_config.moe_top_k
        intermediate_size = config.ffn_config.ffn_hidden_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] == "JambaForCausalLM":
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] in (
        "DeepseekV3ForCausalLM",
        "DeepseekV2ForCausalLM",
@ -591,17 +594,14 @@ def main(args: argparse.Namespace):
        E = config.n_routed_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
        E = config.num_experts
        topk = config.moe_topk[0]
        intermediate_size = config.moe_intermediate_size[0]
-        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    else:
        # Support for llama4
        config = config.get_text_config()
@ -609,8 +609,14 @@ def main(args: argparse.Namespace):
        E = config.num_local_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
+    enable_ep = bool(args.enable_expert_parallel)
+    if enable_ep:
+        ensure_divisibility(E, args.tp_size, "Number of experts")
+        E = E // args.tp_size
+        shard_intermediate_size = 2 * intermediate_size
+    else:
+        ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
-    ensure_divisibility(intermediate_size, args.tp_size)
    hidden_size = config.hidden_size
    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
@ -672,7 +678,11 @@ def main(args: argparse.Namespace):
        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
        search_space = get_configs_compute_bound(is_fp16, block_quant_shape)
        print(f"Start tuning over {len(search_space)} configurations...")
-
+        if use_deep_gemm:
+            raise ValueError(
+                "Tuning with --use-deep-gemm is not supported as it only tunes Triton "
+                "kernels. Please remove the flag."
+            )
        start = time.time()
        configs = _distribute(
            "tune",
@ -706,6 +716,7 @@ def main(args: argparse.Namespace):
            use_fp8_w8a8,
            use_int8_w8a16,
            block_quant_shape,
+            args.save_dir,
        )
        end = time.time()
        print(f"Tuning took {end - start:.2f} seconds")
@ -742,10 +753,14 @@ if __name__ == "__main__":
    parser.add_argument(
        "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
    )
+    parser.add_argument("--enable-expert-parallel", "-enable-ep", action="store_true")
    parser.add_argument(
        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
    )
    parser.add_argument("--use-deep-gemm", action="store_true")
+    parser.add_argument(
+        "--save-dir", type=str, default="./", help="Directory to save tuned results"
+    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--batch-size", type=int, nargs="+", required=False)
    parser.add_argument("--tune", action="store_true")
--- a/benchmarks/kernels/benchmark_mrope.py
+++ b/benchmarks/kernels/benchmark_mrope.py
@ -0,0 +1,328 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# This script benchmarks the mrope kernel (mainly for Qwen2VL and Qwen2.5VL models).
+# It generates test data, runs benchmarks, and saves results to a CSV file.
+#
+# The CSV file (named with current date/time) contains these columns:
+# model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
+# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
+# torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
+# speedup
+#
+# == Usage Examples ==
+#
+# Single model benchmark:
+# python3 benchmark_mrope.py --model-name Qwen/Qwen2-VL-7B-Instruct --tp-size 1 \
+#   --warmup-iter 10 --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models benchmark:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different TP sizes:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 2 4 8 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different token counts:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024 4096 16384
+import csv
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import numpy as np
+import torch
+
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
+from vllm.utils import FlexibleArgumentParser
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_test_data(
+    num_tokens: int,
+    num_q_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    max_position_embeddings: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    """Generate test data for given configuration."""
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(
+        0, max_position_embeddings // 4, (3, num_tokens), device=device
+    )
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
+    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
+
+    return positions, query, key
+
+
+def calculate_stats(times: list[float]) -> dict[str, float]:
+    """Calculate statistics from a list of times."""
+    times_array = np.array(times)
+    return {
+        "mean": np.mean(times_array),
+        "median": np.median(times_array),
+        "p99": np.percentile(times_array, 99),
+        "min": np.min(times_array),
+        "max": np.max(times_array),
+    }
+
+
+def benchmark_mrope(
+    model_name: str,
+    num_tokens: int,
+    head_dim: int,
+    tp_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    max_position: int = 8192,
+    rope_theta: float = 10000,
+    is_neox_style: bool = True,
+    rope_scaling: dict[str, Any] = None,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 0,
+    warmup_iter: int = 10,
+    benchmark_iter: int = 100,
+    csv_writer=None,
+):
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    # the parameters to compute the q k v size based on tp_size
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    print(80 * "=")
+    print(
+        f"Evaluating model: {model_name} "
+        f"with tp_size: {tp_size} "
+        f"and num_tokens: {num_tokens}, "
+        f"dtype: {dtype}"
+    )
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(
+        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
+    )
+
+    # Warm up
+    for _ in range(warmup_iter):
+        mrope_helper_class.forward_native(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+        mrope_helper_class.forward_cuda(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+    torch.cuda.synchronize()
+
+    # Time reference implementation
+    torch_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+
+        mrope_helper_class.forward_native(
+            positions,
+            query_clone,
+            key_clone,
+        )
+
+        torch.cuda.synchronize()
+        torch_times.append(time.time() - start_time)
+
+    # Time triton kernel implementation
+    triton_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+        mrope_helper_class.forward_cuda(
+            positions,
+            query_clone,
+            key_clone,
+        )
+        torch.cuda.synchronize()
+        triton_times.append(time.time() - start_time)
+
+    # Calculate statistics
+    torch_stats = calculate_stats(torch_times)
+    triton_stats = calculate_stats(triton_times)
+    print(f"\nPerformance for config ({num_tokens}, {num_heads}, {num_kv_heads}):")
+
+    print(
+        f"Torch implementation: "
+        f"mean={torch_stats['mean']:.8f}s, "
+        f"median={torch_stats['median']:.8f}s, "
+        f"p99={torch_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton implementation: "
+        f"mean={triton_stats['mean']:.8f}s, "
+        f"median={triton_stats['median']:.8f}s, "
+        f"p99={triton_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton Speedup over Torch: {torch_stats['mean'] / triton_stats['mean']:.8f}x"
+    )
+
+    # Write to CSV
+    if csv_writer:
+        row = [
+            model_name,
+            tp_size,
+            num_tokens,
+            num_heads,
+            num_kv_heads,
+            head_dim,
+            max_position,
+            rope_theta,
+            is_neox_style,
+            str(rope_scaling),
+            str(dtype).split(".")[-1],
+            torch_stats["mean"],
+            torch_stats["median"],
+            torch_stats["p99"],
+            torch_stats["min"],
+            torch_stats["max"],
+            triton_stats["mean"],
+            triton_stats["median"],
+            triton_stats["p99"],
+            triton_stats["min"],
+            triton_stats["max"],
+            torch_stats["mean"] / triton_stats["mean"],  # speedup
+        ]
+        csv_writer.writerow(row)
+
+    return torch_stats, triton_stats
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark the rotary embedding kernels."
+    )
+    parser.add_argument("--model-name", type=str, default="")
+    parser.add_argument("--tp-size", type=int, default=1)
+    parser.add_argument("--warmup-iter", type=int, default=10)
+    parser.add_argument("--benchmark-iter", type=int, default=100)
+    parser.add_argument("--dtype", type=str, choices=["bfloat16"], default="bfloat16")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num-tokens", type=int, nargs="+", required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--output-csv", type=str, default="mrope_benchmark_results.csv")
+    args = parser.parse_args()
+    print(args)
+
+    # Create CSV file for results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    csv_filename = f"{os.path.splitext(args.output_csv)[0]}_{timestamp}.csv"
+
+    with open(csv_filename, "w", newline="") as csvfile:
+        csv_writer = csv.writer(csvfile)
+        # Write header
+        header = [
+            "model_name",
+            "tp_size",
+            "num_tokens",
+            "num_heads",
+            "num_kv_heads",
+            "head_dim",
+            "max_position",
+            "rope_theta",
+            "is_neox_style",
+            "rope_scaling",
+            "dtype",
+            "torch_mean",
+            "torch_median",
+            "torch_p99",
+            "torch_min",
+            "torch_max",
+            "triton_mean",
+            "triton_median",
+            "triton_p99",
+            "triton_min",
+            "triton_max",
+            "speedup",
+        ]
+        csv_writer.writerow(header)
+
+        model_tp_dict = {}
+        if args.model_name == "":
+            model_tp_dict = {
+                "Qwen/Qwen2-VL-2B-Instruct": [1],
+                "Qwen/Qwen2-VL-7B-Instruct": [1],
+                "Qwen/Qwen2-VL-72B-Instruct": [2, 4, 8],
+                "Qwen/Qwen2.5-VL-3B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-7B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-72B-Instruct": [2, 4, 8],
+            }
+        else:
+            model_tp_dict[args.model_name] = [args.tp_size]
+
+        if args.num_tokens is None:
+            num_tokens_list = [2**i for i in range(0, 18)]
+        else:
+            num_tokens_list = args.num_tokens
+
+        for model_name, tp_list in model_tp_dict.items():
+            config = get_config(model_name, trust_remote_code=args.trust_remote_code)
+            for tp_size in tp_list:
+                # get the model config
+                total_num_kv_heads = config.num_key_value_heads
+                total_num_heads = config.num_attention_heads
+                num_heads = total_num_heads // tp_size
+                num_kv_heads = max(1, total_num_kv_heads // tp_size)
+                head_dim = config.hidden_size // total_num_heads
+                q_size = num_heads * head_dim
+                kv_size = num_kv_heads * head_dim
+                is_neox_style = True
+                rope_theta = config.rope_theta
+                max_position = config.max_position_embeddings
+
+                for num_tokens in num_tokens_list:
+                    benchmark_mrope(
+                        model_name=model_name,
+                        num_tokens=num_tokens,
+                        head_dim=head_dim,
+                        tp_size=tp_size,
+                        num_heads=num_heads,
+                        num_kv_heads=num_kv_heads,
+                        max_position=max_position,
+                        rope_theta=rope_theta,
+                        is_neox_style=is_neox_style,
+                        rope_scaling=config.rope_scaling,
+                        dtype=getattr(torch, args.dtype),
+                        seed=args.seed,
+                        warmup_iter=args.warmup_iter,
+                        benchmark_iter=args.benchmark_iter,
+                        csv_writer=csv_writer,
+                    )
+
+    print(f"Benchmark results saved to {csv_filename}")
--- a/benchmarks/kernels/benchmark_silu_mul_fp8_quant.py
+++ b/benchmarks/kernels/benchmark_silu_mul_fp8_quant.py
@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import time
+
+import torch
+
+from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+    silu_mul_fp8_quant_deep_gemm,
+)
+from vllm.platforms import current_platform
+
+
+def benchmark(E, T, H, G=128, runs=50):
+    current_platform.seed_everything(42)
+    y = torch.randn((E, T, 2 * H), dtype=torch.bfloat16, device="cuda")
+    tokens_per_expert = torch.randint(
+        T // 2, T, size=(E,), dtype=torch.int32, device="cuda"
+    )
+
+    # Warmup
+    for _ in range(10):
+        silu_mul_fp8_quant_deep_gemm(y, tokens_per_expert, group_size=G)
+        torch.cuda.synchronize()
+
+    # Benchmark
+    torch.cuda.synchronize()
+    start = time.perf_counter()
+    for _ in range(runs):
+        silu_mul_fp8_quant_deep_gemm(y, tokens_per_expert, group_size=G)
+    torch.cuda.synchronize()
+
+    avg_time = (time.perf_counter() - start) / runs * 1000
+
+    # Calculate actual work done (only count valid tokens)
+    actual_tokens = tokens_per_expert.sum().item()
+    actual_elements = actual_tokens * H
+
+    # GFLOPS: operations per element = exp + 3 muls + 1 div + quantization ops ≈ 8 ops
+    ops_per_element = 8
+    total_ops = actual_elements * ops_per_element
+    gflops = total_ops / (avg_time / 1000) / 1e9
+
+    # Memory bandwidth: bfloat16 inputs (2 bytes), fp8 output (1 byte), scales (4 bytes)
+    input_bytes = actual_tokens * 2 * H * 2  # 2*H bfloat16 inputs
+    output_bytes = actual_tokens * H * 1  # H fp8 outputs
+    scale_bytes = actual_tokens * (H // G) * 4  # scales in float32
+    total_bytes = input_bytes + output_bytes + scale_bytes
+    memory_bw = total_bytes / (avg_time / 1000) / 1e9
+
+    return avg_time, gflops, memory_bw
+
+
+configs = [
+    (8, 32, 1024),
+    (16, 64, 2048),
+    (32, 128, 4096),
+    # DeepSeekV3 Configs
+    (256, 16, 7168),
+    (256, 32, 7168),
+    (256, 64, 7168),
+    (256, 128, 7168),
+    (256, 256, 7168),
+    (256, 512, 7168),
+    (256, 1024, 7168),
+]
+
+print(f"GPU: {torch.cuda.get_device_name()}")
+print(f"{'Config':<20} {'Time(ms)':<10} {'GFLOPS':<10} {'GB/s':<10}")
+print("-" * 50)
+
+for E, T, H in configs:
+    try:
+        time_ms, gflops, gbps = benchmark(E, T, H)
+        print(f"E={E:3d},T={T:4d},H={H:4d} {time_ms:8.3f} {gflops:8.1f} {gbps:8.1f}")
+    except Exception:
+        print(f"E={E:3d},T={T:4d},H={H:4d} FAILED")
--- a/benchmarks/kernels/benchmark_trtllm_attention.py
+++ b/benchmarks/kernels/benchmark_trtllm_attention.py
@ -1,254 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import csv
-import os
-import random
-from datetime import datetime
-
-import flashinfer
-import torch
-
-FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
-
-# KV Cache Layout for TRT-LLM
-# kv_cache_shape = (num_blocks, 2, num_kv_heads, page_size, head_dim)
-
-
-def to_float8(x, dtype=torch.float8_e4m3fn):
-    finfo = torch.finfo(dtype)
-    min_val, max_val = x.aminmax()
-    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
-    scale = finfo.max / amax * 0.1
-    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
-    return x_scl_sat.to(dtype), scale.float().reciprocal()
-
-
-@torch.no_grad()
-def benchmark_decode(
-    num_seqs,
-    max_seq_len,
-    page_size=16,
-    dtype=torch.bfloat16,
-    kv_layout="HND",
-    num_kv_heads=8,
-    kv_cache_dtype="auto",
-    head_dim=128,
-    warmup=10,
-    trials=20,
-):
-    torch.set_default_device("cuda")
-    device = "cuda"
-    torch.manual_seed(0)
-
-    # Currently only HEAD_GRP_SIZE == 8 is supported
-    HEAD_GRP_SIZE = 8
-    MAX_SEQ_LEN = max_seq_len
-
-    # large number to reduce kv_cache reuse
-    NUM_BLOCKS = int(256000 / page_size)
-
-    workspace_buffer = torch.empty(1024 * 1024 * 1024, dtype=torch.int8, device=device)
-
-    # For decode, batch_size is num_decode_token
-    num_qo_heads = num_kv_heads * HEAD_GRP_SIZE
-    sm_scale = float(1.0 / (head_dim**0.5))
-    q = torch.randn(num_seqs, num_qo_heads, head_dim, device=device, dtype=dtype)
-    kv_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_seqs)]
-
-    max_kv_len = max(kv_lens)
-    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int, device=device)
-    max_num_blocks_per_seq = (max_kv_len + page_size - 1) // page_size
-
-    block_tables = torch.randint(
-        0, NUM_BLOCKS, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
-    )
-
-    kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, page_size, head_dim)
-    kv_cache = torch.randn(size=kv_cache_shape, device=device, dtype=dtype)
-    k_scale = v_scale = 1.0
-
-    if kv_cache_dtype.startswith("fp8"):
-        kv_cache, _ = to_float8(kv_cache)
-
-    output_trtllm = torch.empty(q.shape, dtype=dtype)
-
-    # Benchmark TRT decode
-    def trt_decode():
-        return flashinfer.decode.trtllm_batch_decode_with_kv_cache(
-            q,
-            kv_cache,
-            workspace_buffer,
-            block_tables,
-            kv_lens_tensor,
-            max_kv_len,
-            bmm1_scale=k_scale * sm_scale,
-            bmm2_scale=v_scale,
-            out=output_trtllm,
-        )
-
-    def time_fn(fn, warmup=10, trials=20):
-        torch.cuda.synchronize()
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-        times = []
-        for i in range(warmup):
-            fn()
-        for i in range(trials):
-            start.record()
-            fn()
-            end.record()
-            torch.cuda.synchronize()
-            times.append(start.elapsed_time(end))  # ms
-        return sum(times) / len(times), torch.std(torch.tensor(times))
-
-    # TRT Decode
-    trt_mean, trt_std = time_fn(trt_decode)
-
-    kv_indptr = [0]
-    kv_indices = []
-    kv_last_page_lens = []
-    for i in range(num_seqs):
-        seq_len = kv_lens[i]
-        assert seq_len > 0
-        num_blocks = (seq_len + page_size - 1) // page_size
-        kv_indices.extend(block_tables[i, :num_blocks])
-        kv_indptr.append(kv_indptr[-1] + num_blocks)
-        kv_last_page_len = seq_len % page_size
-        if kv_last_page_len == 0:
-            kv_last_page_len = page_size
-        kv_last_page_lens.append(kv_last_page_len)
-
-    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
-    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
-    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
-
-    output_baseline = torch.empty(q.shape, dtype=dtype)
-
-    wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
-        workspace_buffer,
-        kv_layout,
-        use_tensor_cores=((num_qo_heads // num_kv_heads) > 4),
-    )
-
-    wrapper.plan(
-        kv_indptr,
-        kv_indices,
-        kv_last_page_lens,
-        num_qo_heads,
-        num_kv_heads,
-        head_dim,
-        page_size,
-        "NONE",
-        q_data_type=dtype,
-        kv_data_type=torch.float8_e4m3fn if kv_cache_dtype.startswith("fp8") else dtype,
-    )
-
-    def baseline_decode():
-        return wrapper.run(q, kv_cache, sm_scale, k_scale, v_scale, output_baseline)
-
-    baseline_mean, baseline_std = time_fn(baseline_decode)
-
-    # Calculate percentage speedup (positive means TRT is faster)
-    speedup_percent = (baseline_mean - trt_mean) / baseline_mean
-
-    print(
-        f"\t{num_seqs}\t{max_seq_len}\t{trt_mean:.3f}\t{trt_std.item():.3f}"
-        f"\t{baseline_mean:.3f}\t{baseline_std.item():.3f}\t{speedup_percent:.3f}"
-    )
-
-    # Return results for CSV writing
-    return {
-        "num_seqs": num_seqs,
-        "trt_mean": trt_mean,
-        "trt_std": trt_std.item(),
-        "baseline_mean": baseline_mean,
-        "baseline_std": baseline_std.item(),
-        "speedup_percent": speedup_percent,
-        "q_dtype": str(dtype),
-        "kv_cache_dtype": kv_cache_dtype,
-        "page_size": page_size,
-        "num_kv_heads": num_kv_heads,
-        "head_dim": head_dim,
-        "max_seq_len": max_seq_len,
-    }
-
-
-def write_results_to_csv(results, filename=None):
-    """Write benchmark results to CSV file."""
-    if filename is None:
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"flashinfer_trtllm_benchmark_{timestamp}.csv"
-
-    fieldnames = [
-        "num_seqs",
-        "trt_mean",
-        "trt_std",
-        "baseline_mean",
-        "baseline_std",
-        "speedup_percent",
-        "q_dtype",
-        "kv_cache_dtype",
-        "page_size",
-        "num_kv_heads",
-        "head_dim",
-        "max_seq_len",
-    ]
-
-    file_exists = os.path.exists(filename)
-
-    with open(filename, "a", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-
-        if not file_exists:
-            writer.writeheader()
-
-        for result in results:
-            writer.writerow(result)
-
-    print(f"Results written to {filename}")
-
-
-if __name__ == "__main__":
-    num_seqs = [1, 4, 8, 16, 32, 64, 128, 256]
-    max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
-    all_results = []
-
-    print(
-        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: bfloat16, "
-        "output_dtype: bfloat16"
-    )
-    print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
-        "baseline_std\tspeedup_percent"
-    )
-    for max_seq_len in max_seq_lens:
-        for bs in num_seqs:
-            result = benchmark_decode(
-                bs,
-                max_seq_len,
-                dtype=torch.bfloat16,
-                kv_cache_dtype="auto",
-            )
-            all_results.append(result)
-
-    print(
-        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: fp8, "
-        "output_dtype: bfloat16"
-    )
-    print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
-        "baseline_std\tspeedup_percent"
-    )
-    for max_seq_len in max_seq_lens:
-        for bs in num_seqs:
-            result = benchmark_decode(
-                bs,
-                max_seq_len,
-                dtype=torch.bfloat16,
-                kv_cache_dtype="fp8",
-            )
-            all_results.append(result)
-
-    # Write all results to CSV
-    write_results_to_csv(all_results)
--- a/benchmarks/kernels/benchmark_trtllm_decode_attention.py
+++ b/benchmarks/kernels/benchmark_trtllm_decode_attention.py
@ -0,0 +1,292 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import csv
+import os
+from datetime import datetime
+from typing import Optional
+
+import flashinfer
+import torch
+
+from vllm.utils import round_up
+
+FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
+FP8_DTYPE = torch.float8_e4m3fn
+FP4_DTYPE = torch.uint8
+
+
+def to_float8(x, dtype=torch.float8_e4m3fn):
+    finfo = torch.finfo(dtype)
+    min_val, max_val = x.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
+    scale = finfo.max / amax * 0.1
+    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
+    return x_scl_sat.to(dtype), scale.float().reciprocal()
+
+
+@torch.no_grad()
+def benchmark_decode(
+    dtype: torch.dtype,
+    quant_dtypes: tuple[
+        Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
+    ],
+    batch_size: int,
+    max_seq_len: int,
+    num_heads: tuple[int, int] = (64, 8),
+    head_size: int = 128,
+    kv_layout: str = "HND",
+    block_size: int = 16,
+    warmup: int = 10,
+    trials: int = 20,
+):
+    torch.set_default_device("cuda")
+    torch.manual_seed(0)
+
+    q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
+    q_quant_dtype = q_quant_dtype or dtype
+    kv_quant_dtype = kv_quant_dtype or dtype
+    o_quant_dtype = o_quant_dtype or dtype
+
+    num_qo_heads, num_kv_heads = num_heads
+    assert num_qo_heads % num_kv_heads == 0
+
+    sm_scale = float(1.0 / (head_size**0.5))
+
+    # large number to reduce kv_cache reuse
+    NUM_BLOCKS = int(256000 / block_size)
+
+    kv_cache_shape = None
+    if kv_layout == "NHD":
+        kv_cache_shape = (NUM_BLOCKS, 2, block_size, num_kv_heads, head_size)
+    elif kv_layout == "HND":
+        kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, block_size, head_size)
+    else:
+        raise ValueError(f"Invalid kv_layout: {kv_layout}")
+
+    # Always using 1.0 scale to reflect the real perf in benchmarking
+    q_scale = 1.0
+    ref_query = torch.randn(batch_size, num_qo_heads, head_size, dtype=dtype)
+    if q_quant_dtype == FP8_DTYPE:
+        query, _ = to_float8(ref_query)
+    else:
+        query = ref_query
+
+    kv_lens = torch.randint(1, max_seq_len, (batch_size,), dtype=torch.int32)
+    kv_lens[-1] = max_seq_len
+
+    seq_lens = kv_lens
+    max_seq_len = torch.max(seq_lens).item()
+
+    # Always using 1.0 scale to reflect the real perf in benchmarking
+    k_scale = v_scale = 1.0
+    ref_kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
+    if kv_quant_dtype == FP8_DTYPE:
+        kv_cache, _ = to_float8(ref_kv_cache)
+    else:
+        kv_cache = ref_kv_cache
+
+    max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
+    block_tables = torch.randint(
+        0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
+    )
+    kv_indptr = [0]
+    kv_indices = []
+    kv_last_page_lens = []
+    for i in range(batch_size):
+        seq_len = seq_lens[i]
+        assert seq_len > 0
+        num_blocks = (seq_len + block_size - 1) // block_size
+        kv_indices.extend(block_tables[i, :num_blocks])
+        kv_indptr.append(kv_indptr[-1] + num_blocks)
+        kv_last_page_len = seq_len % block_size
+        if kv_last_page_len == 0:
+            kv_last_page_len = block_size
+        kv_last_page_lens.append(kv_last_page_len)
+
+    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
+    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
+    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
+    workspace_buffer = torch.zeros(1024 * 1024 * 1024, dtype=torch.int8)
+
+    wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
+        workspace_buffer,
+        kv_layout,
+        use_tensor_cores=True,
+    )
+    wrapper.plan(
+        kv_indptr,
+        kv_indices,
+        kv_last_page_lens,
+        num_qo_heads,
+        num_kv_heads,
+        head_size,
+        block_size,
+        "NONE",
+        sm_scale=sm_scale,
+        q_data_type=dtype,
+        kv_data_type=dtype,
+    )
+
+    def time_fn(fn, warmup=10, trials=20):
+        torch.cuda.synchronize()
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        times = []
+        for i in range(warmup):
+            fn()
+        for i in range(trials):
+            start.record()
+            fn()
+            end.record()
+            torch.cuda.synchronize()
+            times.append(start.elapsed_time(end))  # ms
+        return sum(times) / len(times), torch.std(torch.tensor(times))
+
+    o_scale = 1.0
+    o_sf_scale = None
+    output_baseline = torch.empty(ref_query.shape, dtype=dtype)
+    if o_quant_dtype == FP4_DTYPE:
+        o_sf_scale = 500.0
+        output_trtllm = flashinfer.utils.FP4Tensor(
+            torch.empty(query.shape[:-1] + (query.shape[-1] // 2,), dtype=torch.uint8),
+            torch.empty(
+                (
+                    round_up(query.shape[0], 128),
+                    round_up(query.shape[1] * query.shape[2] // 16, 4),
+                ),
+                dtype=torch.float8_e4m3fn,
+            ),
+        )
+    else:
+        output_trtllm = torch.empty(query.shape, dtype=o_quant_dtype)
+
+    def baseline_decode():
+        return wrapper.run(
+            ref_query,
+            ref_kv_cache,
+            k_scale=k_scale,
+            v_scale=v_scale,
+            out=output_baseline,
+        )
+
+    def trtllm_decode():
+        return flashinfer.decode.trtllm_batch_decode_with_kv_cache(
+            query=query,
+            kv_cache=kv_cache,
+            workspace_buffer=workspace_buffer,
+            block_tables=block_tables,
+            seq_lens=seq_lens,
+            max_seq_len=max_seq_len,
+            bmm1_scale=q_scale * k_scale * sm_scale,
+            bmm2_scale=v_scale / o_scale,
+            o_sf_scale=o_sf_scale,
+            out=output_trtllm,
+        )
+
+    baseline_mean, baseline_std = time_fn(baseline_decode)
+    trtllm_mean, trtllm_std = time_fn(trtllm_decode)
+
+    # Calculate percentage speedup (positive means TRT is faster)
+    speedup_percent = (baseline_mean - trtllm_mean) / baseline_mean
+
+    print(
+        f"\t{batch_size}\t{max_seq_len}\t{trtllm_mean:.3f}\t{trtllm_std.item():.3f}"
+        f"\t{baseline_mean:.3f}\t{baseline_std.item():.3f}\t{speedup_percent:.3f}"
+    )
+
+    # Return results for CSV writing
+    return {
+        "batch_size": batch_size,
+        "trtllm_mean": trtllm_mean,
+        "trtllm_std": trtllm_std.item(),
+        "baseline_mean": baseline_mean,
+        "baseline_std": baseline_std.item(),
+        "speedup_percent": speedup_percent,
+        "q_dtype": str(q_quant_dtype),
+        "kv_cache_dtype": str(kv_quant_dtype),
+        "output_dtype": str(o_quant_dtype),
+        "block_size": block_size,
+        "num_kv_heads": num_kv_heads,
+        "head_size": head_size,
+        "max_seq_len": max_seq_len,
+    }
+
+
+def write_results_to_csv(results, filename=None):
+    """Write benchmark results to CSV file."""
+    if filename is None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"flashinfer_trtllm_benchmark_{timestamp}.csv"
+
+    fieldnames = [
+        "batch_size",
+        "trtllm_mean",
+        "trtllm_std",
+        "baseline_mean",
+        "baseline_std",
+        "speedup_percent",
+        "q_dtype",
+        "kv_cache_dtype",
+        "output_dtype",
+        "block_size",
+        "num_kv_heads",
+        "head_size",
+        "max_seq_len",
+    ]
+
+    file_exists = os.path.exists(filename)
+
+    with open(filename, "a", newline="") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+        if not file_exists:
+            writer.writeheader()
+
+        for result in results:
+            writer.writerow(result)
+
+    print(f"Results written to {filename}")
+
+
+if __name__ == "__main__":
+    batch_sizes = [1, 4, 8, 16, 32, 64, 128, 256]
+    max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
+    all_results = []
+
+    dtype = torch.bfloat16
+    quant_dtypes = [
+        # (q_quant_dtype, kv_quant_dtype, o_quant_dtype)
+        (None, None, None),
+        (None, FP8_DTYPE, None),
+        (FP8_DTYPE, FP8_DTYPE, FP8_DTYPE),
+        (FP8_DTYPE, FP8_DTYPE, FP4_DTYPE),
+    ]
+
+    for quant_dtype in quant_dtypes:
+        q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtype
+        q_quant_dtype = q_quant_dtype or dtype
+        kv_quant_dtype = kv_quant_dtype or dtype
+        o_quant_dtype = o_quant_dtype or dtype
+
+        print(
+            f"Running benchmark for q_dtype = {q_quant_dtype}, "
+            f"kv_cache_dtype: {kv_quant_dtype}, "
+            f"output_dtype: {o_quant_dtype}"
+        )
+        print(
+            "\tbatch_size\tmax_seq_len\ttrtllm_mean\ttrtllm_std\tbaseline_mean\t"
+            "baseline_std\tspeedup_percent"
+        )
+        for max_seq_len in max_seq_lens:
+            for bs in batch_sizes:
+                result = benchmark_decode(
+                    dtype=dtype,
+                    quant_dtypes=quant_dtype,
+                    batch_size=bs,
+                    max_seq_len=max_seq_len,
+                )
+                all_results.append(result)
+
+    # Write all results to CSV
+    write_results_to_csv(all_results)
--- a/benchmarks/kernels/benchmark_trtllm_prefill_attention.py
+++ b/benchmarks/kernels/benchmark_trtllm_prefill_attention.py
@ -0,0 +1,307 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import csv
+import os
+from datetime import datetime
+from typing import Optional
+
+import flashinfer
+import torch
+
+from vllm.utils import round_up
+
+FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
+FP8_DTYPE = torch.float8_e4m3fn
+FP4_DTYPE = torch.uint8
+
+
+def to_float8(x, dtype=torch.float8_e4m3fn):
+    finfo = torch.finfo(dtype)
+    min_val, max_val = x.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
+    scale = finfo.max / amax * 0.1
+    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
+    return x_scl_sat.to(dtype), scale.float().reciprocal()
+
+
+@torch.no_grad()
+def benchmark_prefill(
+    dtype: torch.dtype,
+    quant_dtypes: tuple[
+        Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
+    ],
+    batch_size: int,
+    max_seq_len: int,
+    num_heads: tuple[int, int] = (64, 8),
+    head_size: int = 128,
+    kv_layout: str = "HND",
+    block_size: int = 16,
+    warmup: int = 10,
+    trials: int = 20,
+):
+    torch.set_default_device("cuda")
+    torch.manual_seed(0)
+
+    q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
+    q_quant_dtype = q_quant_dtype or dtype
+    kv_quant_dtype = kv_quant_dtype or dtype
+    o_quant_dtype = o_quant_dtype or dtype
+
+    max_q_len = max_kv_len = max_seq_len
+
+    num_qo_heads, num_kv_heads = num_heads
+    assert num_qo_heads % num_kv_heads == 0
+
+    sm_scale = float(1.0 / (head_size**0.5))
+
+    # large number to reduce kv_cache reuse
+    NUM_BLOCKS = int(256000 / block_size)
+
+    kv_cache_shape = None
+    if kv_layout == "NHD":
+        kv_cache_shape = (NUM_BLOCKS, 2, block_size, num_kv_heads, head_size)
+    elif kv_layout == "HND":
+        kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, block_size, head_size)
+    else:
+        raise ValueError(f"Invalid kv_layout: {kv_layout}")
+
+    q_lens = torch.randint(1, max_q_len, (batch_size,), dtype=torch.int32)
+    q_lens[-1] = max_q_len
+    q_indptr = torch.cat(
+        [
+            torch.tensor([0], dtype=torch.int32),
+            torch.cumsum(q_lens, dim=0, dtype=torch.int32),
+        ]
+    )
+
+    # Always using 1.0 scale to reflect the real perf in benchmarking
+    q_scale = 1.0
+    ref_query = torch.randn(
+        torch.sum(q_lens).item(), num_qo_heads, head_size, dtype=dtype
+    )
+    if q_quant_dtype == FP8_DTYPE:
+        query, _ = to_float8(ref_query)
+    else:
+        query = ref_query
+
+    kv_lens = torch.randint(0, max_kv_len, (batch_size,), dtype=torch.int32)
+    kv_lens[-1] = max_kv_len
+
+    seq_lens = kv_lens + q_lens
+    max_seq_len = torch.max(seq_lens).item()
+
+    # Always using 1.0 scale to reflect the real perf in benchmarking
+    k_scale = v_scale = 1.0
+    ref_kv_cache = torch.randn(kv_cache_shape, dtype=dtype)
+    if kv_quant_dtype == FP8_DTYPE:
+        kv_cache, _ = to_float8(ref_kv_cache)
+    else:
+        kv_cache = ref_kv_cache
+
+    max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
+    block_tables = torch.randint(
+        0, NUM_BLOCKS, (batch_size, max_num_blocks_per_seq), dtype=torch.int32
+    )
+    kv_indptr = [0]
+    kv_indices = []
+    kv_last_page_lens = []
+    for i in range(batch_size):
+        seq_len = seq_lens[i]
+        assert seq_len > 0
+        num_blocks = (seq_len + block_size - 1) // block_size
+        kv_indices.extend(block_tables[i, :num_blocks])
+        kv_indptr.append(kv_indptr[-1] + num_blocks)
+        kv_last_page_len = seq_len % block_size
+        if kv_last_page_len == 0:
+            kv_last_page_len = block_size
+        kv_last_page_lens.append(kv_last_page_len)
+
+    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
+    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
+    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
+    workspace_buffer = torch.zeros(1024 * 1024 * 1024, dtype=torch.int8)
+
+    wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
+        workspace_buffer, kv_layout
+    )
+    wrapper.plan(
+        q_indptr,
+        kv_indptr,
+        kv_indices,
+        kv_last_page_lens,
+        num_qo_heads,
+        num_kv_heads,
+        head_size,
+        block_size,
+        causal=True,
+        sm_scale=sm_scale,
+        q_data_type=dtype,
+        kv_data_type=dtype,
+    )
+
+    def time_fn(fn, warmup=10, trials=20):
+        torch.cuda.synchronize()
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        times = []
+        for i in range(warmup):
+            fn()
+        for i in range(trials):
+            start.record()
+            fn()
+            end.record()
+            torch.cuda.synchronize()
+            times.append(start.elapsed_time(end))  # ms
+        return sum(times) / len(times), torch.std(torch.tensor(times))
+
+    o_scale = 1.0
+    o_sf_scale = None
+    output_baseline = torch.empty(ref_query.shape, dtype=dtype)
+    if o_quant_dtype == FP4_DTYPE:
+        o_sf_scale = 500.0
+        output_trtllm = flashinfer.utils.FP4Tensor(
+            torch.empty(query.shape[:-1] + (query.shape[-1] // 2,), dtype=torch.uint8),
+            torch.empty(
+                (
+                    round_up(query.shape[0], 128),
+                    round_up(query.shape[1] * query.shape[2] // 16, 4),
+                ),
+                dtype=torch.float8_e4m3fn,
+            ),
+        )
+    else:
+        output_trtllm = torch.empty(query.shape, dtype=o_quant_dtype)
+
+    def baseline_prefill():
+        return wrapper.run(
+            ref_query,
+            ref_kv_cache,
+            k_scale=k_scale,
+            v_scale=v_scale,
+            out=output_baseline,
+        )
+
+    def trtllm_prefill():
+        return flashinfer.prefill.trtllm_batch_context_with_kv_cache(
+            query=query,
+            kv_cache=kv_cache,
+            workspace_buffer=workspace_buffer,
+            block_tables=block_tables,
+            seq_lens=seq_lens,
+            max_q_len=max_q_len,
+            max_kv_len=max_seq_len,
+            bmm1_scale=q_scale * k_scale * sm_scale,
+            bmm2_scale=v_scale / o_scale,
+            batch_size=batch_size,
+            cum_seq_lens_q=q_indptr,
+            cum_seq_lens_kv=kv_indptr,
+            o_sf_scale=o_sf_scale,
+            out=output_trtllm,
+        )
+
+    baseline_mean, baseline_std = time_fn(baseline_prefill)
+    trtllm_mean, trtllm_std = time_fn(trtllm_prefill)
+
+    # Calculate percentage speedup (positive means TRT is faster)
+    speedup_percent = (baseline_mean - trtllm_mean) / baseline_mean
+
+    print(
+        f"\t{batch_size}\t{max_seq_len}\t{trtllm_mean:8.3f}\t{trtllm_std.item():8.3f}"
+        f"\t{baseline_mean:8.3f}\t{baseline_std.item():8.3f}\t{speedup_percent:8.3f}"
+    )
+
+    # Return results for CSV writing
+    return {
+        "batch_size": batch_size,
+        "trtllm_mean": trtllm_mean,
+        "trtllm_std": trtllm_std.item(),
+        "baseline_mean": baseline_mean,
+        "baseline_std": baseline_std.item(),
+        "speedup_percent": speedup_percent,
+        "q_dtype": str(q_quant_dtype),
+        "kv_cache_dtype": str(kv_quant_dtype),
+        "output_dtype": str(o_quant_dtype),
+        "block_size": block_size,
+        "num_kv_heads": num_kv_heads,
+        "head_size": head_size,
+        "max_seq_len": max_seq_len,
+    }
+
+
+def write_results_to_csv(results, filename=None):
+    """Write benchmark results to CSV file."""
+    if filename is None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"flashinfer_trtllm_benchmark_{timestamp}.csv"
+
+    fieldnames = [
+        "batch_size",
+        "trtllm_mean",
+        "trtllm_std",
+        "baseline_mean",
+        "baseline_std",
+        "speedup_percent",
+        "q_dtype",
+        "kv_cache_dtype",
+        "output_dtype",
+        "block_size",
+        "num_kv_heads",
+        "head_size",
+        "max_seq_len",
+    ]
+
+    file_exists = os.path.exists(filename)
+
+    with open(filename, "a", newline="") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+        if not file_exists:
+            writer.writeheader()
+
+        for result in results:
+            writer.writerow(result)
+
+    print(f"Results written to {filename}")
+
+
+if __name__ == "__main__":
+    batch_sizes = [1, 4, 8, 16, 32, 64, 128, 256]
+    max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
+    all_results = []
+
+    dtype = torch.bfloat16
+    quant_dtypes = [
+        # (q_quant_dtype, kv_quant_dtype, o_quant_dtype)
+        (None, None, None),
+        (FP8_DTYPE, FP8_DTYPE, FP8_DTYPE),
+        (FP8_DTYPE, FP8_DTYPE, FP4_DTYPE),
+    ]
+
+    for quant_dtype in quant_dtypes:
+        q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtype
+        q_quant_dtype = q_quant_dtype or dtype
+        kv_quant_dtype = kv_quant_dtype or dtype
+        o_quant_dtype = o_quant_dtype or dtype
+
+        print(
+            f"Running benchmark for q_dtype = {q_quant_dtype}, "
+            f"kv_cache_dtype: {kv_quant_dtype}, "
+            f"output_dtype: {o_quant_dtype}"
+        )
+        print(
+            "\tbatch_size\tmax_seq_len\ttrtllm_mean\ttrtllm_std\tbaseline_mean\t"
+            "baseline_std\tspeedup_percent"
+        )
+        for max_seq_len in max_seq_lens:
+            for bs in batch_sizes:
+                result = benchmark_prefill(
+                    dtype=dtype,
+                    quant_dtypes=quant_dtype,
+                    batch_size=bs,
+                    max_seq_len=max_seq_len,
+                )
+                all_results.append(result)
+
+    # Write all results to CSV
+    write_results_to_csv(all_results)
--- a/benchmarks/kernels/benchmark_w8a8_block_fp8.py
+++ b/benchmarks/kernels/benchmark_w8a8_block_fp8.py
@ -11,8 +11,8 @@ from datetime import datetime
 from typing import Any

 import torch
-import tqdm
 import triton
+from tqdm import tqdm

 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
    _w8a8_block_fp8_matmul,
@ -141,6 +141,7 @@ def get_weight_shapes(tp_size):
    # cannot TP
    total = [
        (512 + 64, 7168),
+        (2112, 7168),
        ((128 + 64) * 128, 7168),
        (128 * (128 + 128), 512),
        (7168, 16384),
--- a/benchmarks/kernels/weight_shapes.py
+++ b/benchmarks/kernels/weight_shapes.py
@ -95,4 +95,10 @@ WEIGHT_SHAPES = {
        ([2048, 2816], 1),
        ([1408, 2048], 0),
    ],
+    "CohereLabs/c4ai-command-a-03-2025": [
+        ([12288, 14336], 1),
+        ([12288, 12288], 0),
+        ([12288, 73728], 1),
+        ([36864, 12288], 0),
+    ],
 }
--- a/benchmarks/kv_cache/benchmark_block_pool.py
+++ b/benchmarks/kv_cache/benchmark_block_pool.py
@ -1,108 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import gc
-import time
-from typing import Optional
-
-from tabulate import tabulate
-
-from vllm.utils import FlexibleArgumentParser
-from vllm.v1.core.block_pool import BlockPool
-
-
-class Metric:
-    def __init__(self) -> None:
-        self.cnt: int = 0
-        self.sum_v: int = 0
-        self.max_v: Optional[int] = None
-
-    def update(self, v: int) -> None:
-        self.cnt += 1
-        self.sum_v += v
-        if self.max_v is None:
-            self.max_v = v
-        else:
-            self.max_v = max(self.max_v, v)
-
-    def avg_v(self) -> float:
-        return self.sum_v * 1.0 / self.cnt
-
-
-def main(args):
-    rows = []
-    for allocate_block in args.allocate_blocks:
-        # Enforce a GC collect ahead to minimize the impact among runs
-        gc.collect()
-        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
-
-        get_blocks_metric: Metric = Metric()
-        free_blocks_metric: Metric = Metric()
-        for _ in range(args.num_iteration):
-            t1 = time.monotonic_ns()
-            blocks = block_pool.get_new_blocks(allocate_block)
-            t2 = time.monotonic_ns()
-            block_pool.free_blocks(blocks)
-            t3 = time.monotonic_ns()
-            get_blocks_metric.update(t2 - t1)
-            free_blocks_metric.update(t3 - t2)
-
-        if get_blocks_metric.max_v is not None and free_blocks_metric.max_v is not None:
-            rows.append(
-                [
-                    get_blocks_metric.cnt,
-                    args.num_gpu_blocks,
-                    allocate_block,
-                    get_blocks_metric.avg_v() / 1000000,
-                    get_blocks_metric.max_v / 1000000.0,
-                    free_blocks_metric.avg_v() / 1000000,
-                    free_blocks_metric.max_v / 1000000.0,
-                ]
-            )
-        else:
-            print(
-                "No valid metrics found."
-                f" {get_blocks_metric.max_v=} {free_blocks_metric.max_v=}"
-            )
-
-    print(
-        tabulate(
-            rows,
-            headers=[
-                "Iterations",
-                "Total\nBlocks",
-                "Allocated\nBlocks",
-                "Get Blocks\nAvg (ms)",
-                "Get Blocks\nMax (ms)",
-                "Free Blocks\nAvg (ms)",
-                "Free Blocks\nMax (ms)",
-            ],
-            tablefmt="grid",
-            floatfmt=".6f",
-        )
-    )
-
-
-def invoke_main() -> None:
-    parser = FlexibleArgumentParser(
-        description="Benchmark the performance of BlockPool for KV Cache."
-    )
-    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
-    parser.add_argument(
-        "--num-iteration",
-        type=int,
-        default=1000,
-        help="Number of iterations to run to stablize final data readings",
-    )
-    parser.add_argument(
-        "--allocate-blocks",
-        type=int,
-        nargs="*",
-        default=[10, 50, 100, 500, 1000],
-        help="Number of blocks to allocate",
-    )
-    args = parser.parse_args()
-    main(args)
-
-
-if __name__ == "__main__":
-    invoke_main()  # pragma: no cover
--- a/benchmarks/multi_turn/README.md
+++ b/benchmarks/multi_turn/README.md
@ -0,0 +1,73 @@
+# Benchmark KV Cache Offloading with Multi-Turn Conversations
+
+The requirements (pip) for `benchmark_serving_multi_turn.py` can be found in `requirements.txt`
+
+First start serving your model
+
+```bash
+export MODEL_PATH=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
+
+vllm serve $MODEL_PATH --served-model-name Llama --disable-log-requests
+```
+
+The variable `MODEL_PATH` should be a path to the model files (e.g. downloaded from huggingface).
+
+## Synthetic Multi-Turn Conversations
+
+Download the following text file (used for generation of synthetic conversations)
+
+```bash
+wget https://www.gutenberg.org/ebooks/1184.txt.utf-8
+mv 1184.txt.utf-8 pg1184.txt
+```
+
+The filename `pg1184.txt` is used in `generate_multi_turn.json` (see `"text_files"`).
+
+But you may use other text files if you prefer (using this specific file is not required).
+
+Then run the benchmarking script
+
+```bash
+export MODEL_PATH=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
+
+python benchmark_serving_multi_turn.py --model $MODEL_PATH --served-model-name Llama \
+--input-file generate_multi_turn.json --num-clients 2 --max-active-conversations 6
+```
+
+You can edit the file `generate_multi_turn.json` to change the conversation parameters (number of turns, etc.).
+
+If successful, you will see the following output
+
+```bash
+----------------------------------------------------------------------------------------------------
+Statistics summary:
+runtime_sec = 215.810
+requests_per_sec = 0.769
+----------------------------------------------------------------------------------------------------
+                   count     mean     std      min      25%      50%      75%      90%      99%      max
+ttft_ms            166.0    78.22   67.63    45.91    59.94    62.26    64.43    69.66   353.18   567.54
+tpot_ms            166.0    25.37    0.57    24.40    25.07    25.31    25.50    25.84    27.50    28.05
+latency_ms         166.0  2591.07  326.90  1998.53  2341.62  2573.01  2860.10  3003.50  3268.46  3862.94
+input_num_turns    166.0     7.43    4.57     1.00     3.00     7.00    11.00    13.00    17.00    17.00
+input_num_tokens   166.0  2006.20  893.56   522.00  1247.75  2019.00  2718.00  3233.00  3736.45  3899.00
+output_num_tokens  166.0   100.01   11.80    80.00    91.00    99.00   109.75   116.00   120.00   120.00
+output_num_chunks  166.0    99.01   11.80    79.00    90.00    98.00   108.75   115.00   119.00   119.00
+----------------------------------------------------------------------------------------------------
+```
+
+## ShareGPT Conversations
+
+To run with the ShareGPT data, download the following ShareGPT dataset:
+`https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json`
+
+Use the `convert_sharegpt_to_openai.py` script to convert the dataset to a format supported by `benchmark_serving_multi_turn.py`
+
+```bash
+python convert_sharegpt_to_openai.py sharegpt_20230401_clean_lang_split.json sharegpt_conv_128.json --seed=99 --max-items=128
+```
+
+The script will convert the ShareGPT dataset to a dataset with the standard user/assistant roles.
+
+The flag `--max-items=128` is used to sample 128 conversations from the original dataset (change as needed).
+
+Use the output JSON file `sharegpt_conv_128.json` as the `--input-file` for `benchmark_serving_multi_turn.py`.
--- a/benchmarks/multi_turn/bench_dataset.py
+++ b/benchmarks/multi_turn/bench_dataset.py
@ -0,0 +1,493 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+from statistics import mean
+from typing import Any, NamedTuple, Optional, Union
+
+import numpy as np  # type: ignore
+import pandas as pd  # type: ignore
+from bench_utils import (
+    TEXT_SEPARATOR,
+    Color,
+    logger,
+)
+from transformers import AutoTokenizer  # type: ignore
+
+# Conversation ID is a string (e.g: "UzTK34D")
+ConvId = str
+
+# A list of dicts (dicts with keys "id" and "messages")
+ShareGptConversations = list[dict[str, Any]]
+
+# A list of dicts (dicts with keys "role" and "content")
+MessagesList = list[dict[str, str]]
+
+# Map conversation ID to conversation messages
+ConversationsMap = list[ConvId, MessagesList]
+
+
+class Distribution(ABC):
+    @abstractmethod
+    def sample(self, size: int = 1) -> np.ndarray:
+        pass
+
+
+class UniformDistribution(Distribution):
+    def __init__(
+        self,
+        min_val: Union[int, float],
+        max_val: Union[int, float],
+        is_integer: bool = True,
+    ) -> None:
+        self.min_val = min_val
+        self.max_val = max_val
+        self.is_integer = is_integer
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        if self.is_integer:
+            return np.random.randint(
+                int(self.min_val), int(self.max_val + 1), size=size
+            )
+        else:
+            return np.random.uniform(self.min_val, self.max_val, size=size)
+
+    def __repr__(self) -> str:
+        return f"UniformDistribution[{self.min_val}, {self.max_val}]"
+
+
+class ConstantDistribution(Distribution):
+    def __init__(self, value: Union[int, float]) -> None:
+        self.value = value
+        self.max_val = value
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        return np.full(shape=size, fill_value=self.value)
+
+    def __repr__(self) -> str:
+        return f"Constant[{self.value}]"
+
+
+class ZipfDistribution(Distribution):
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
+        self.alpha = alpha
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.zipf(self.alpha, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+        return samples
+
+    def __repr__(self) -> str:
+        return f"ZipfDistribution[{self.alpha}]"
+
+
+class PoissonDistribution(Distribution):
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
+        self.alpha = alpha
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.poisson(self.alpha, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+        return samples
+
+    def __repr__(self) -> str:
+        return f"PoissonDistribution[{self.alpha}]"
+
+
+class LognormalDistribution(Distribution):
+    def __init__(
+        self, mean: float, sigma: float, max_val: Optional[int] = None
+    ) -> None:
+        self.mean = mean
+        self.sigma = sigma
+        self.max_val = max_val
+
+    def sample(self, size: int = 1) -> np.ndarray:
+        samples = np.random.lognormal(mean=self.mean, sigma=self.sigma, size=size)
+        if self.max_val:
+            samples = np.minimum(samples, self.max_val)
+
+        return np.round(samples).astype(int)
+
+    def __repr__(self) -> str:
+        return f"LognormalDistribution[{self.mean}, {self.sigma}]"
+
+
+class GenConvArgs(NamedTuple):
+    num_conversations: int
+    text_files: list[str]
+    input_num_turns: Distribution
+    input_common_prefix_num_tokens: Distribution
+    input_prefix_num_tokens: Distribution
+    input_num_tokens: Distribution
+    output_num_tokens: Distribution
+    print_stats: bool
+
+
+def verify_field_exists(
+    conf: dict, field_name: str, section: str, subsection: str
+) -> None:
+    if field_name not in conf:
+        raise ValueError(
+            f"Missing field '{field_name}' in {section=} and {subsection=}"
+        )
+
+
+def get_random_distribution(
+    conf: dict, section: str, subsection: str, optional: bool = False
+) -> Distribution:
+    # section can be "prompt_input" or "prompt_output" (both required)
+    conf = conf[section]
+
+    if optional and subsection not in conf:
+        # Optional subsection, if not found assume the value is always 0
+        return ConstantDistribution(0)
+
+    # subsection can be "num_turns", "num_tokens" or "prefix_num_tokens"
+    if subsection not in conf:
+        raise ValueError(f"Missing subsection {subsection} in section {section}")
+
+    conf = conf[subsection]
+
+    distribution = conf.get("distribution")
+    if distribution is None:
+        raise ValueError(
+            f"Missing field 'distribution' in {section=} and {subsection=}"
+        )
+
+    if distribution == "constant":
+        verify_field_exists(conf, "value", section, subsection)
+        return ConstantDistribution(conf["value"])
+
+    elif distribution == "zipf":
+        verify_field_exists(conf, "alpha", section, subsection)
+        max_val = conf.get("max", None)
+        return ZipfDistribution(conf["alpha"], max_val=max_val)
+
+    elif distribution == "poisson":
+        verify_field_exists(conf, "alpha", section, subsection)
+        max_val = conf.get("max", None)
+        return PoissonDistribution(conf["alpha"], max_val=max_val)
+
+    elif distribution == "lognormal":
+        verify_field_exists(conf, "mean", section, subsection)
+        verify_field_exists(conf, "sigma", section, subsection)
+        max_val = conf.get("max", None)
+        return LognormalDistribution(conf["mean"], conf["sigma"], max_val=max_val)
+
+    elif distribution == "uniform":
+        verify_field_exists(conf, "min", section, subsection)
+        verify_field_exists(conf, "max", section, subsection)
+
+        min_value = conf["min"]
+        max_value = conf["max"]
+
+        assert min_value > 0
+        assert min_value <= max_value
+
+        is_integer = isinstance(min_value, int) and isinstance(max_value, int)
+        return UniformDistribution(min_value, max_value, is_integer)
+    else:
+        raise ValueError(f"Unknown distribution: {distribution}")
+
+
+def parse_input_json_file(conf: dict) -> GenConvArgs:
+    # Validate the input file
+    assert isinstance(conf, dict)
+    required_fields = [
+        "filetype",
+        "num_conversations",
+        "text_files",
+        "prompt_input",
+        "prompt_output",
+    ]
+    for field in required_fields:
+        assert field in conf, f"Missing field {field} in input {conf}"
+
+    assert conf["filetype"] == "generate_conversations"
+
+    assert conf["num_conversations"] > 0, "num_conversations should be larger than zero"
+
+    text_files = conf["text_files"]
+
+    assert isinstance(text_files, list), "Field 'text_files' should be a list"
+    assert len(text_files) > 0, (
+        "Field 'text_files' should be a list with at least one file"
+    )
+
+    # Parse the parameters for the prompt input/output workload
+    input_num_turns = get_random_distribution(conf, "prompt_input", "num_turns")
+    input_num_tokens = get_random_distribution(conf, "prompt_input", "num_tokens")
+    input_common_prefix_num_tokens = get_random_distribution(
+        conf, "prompt_input", "common_prefix_num_tokens", optional=True
+    )
+    input_prefix_num_tokens = get_random_distribution(
+        conf, "prompt_input", "prefix_num_tokens"
+    )
+    output_num_tokens = get_random_distribution(conf, "prompt_output", "num_tokens")
+
+    print_stats: bool = conf.get("print_stats", False)
+    assert isinstance(print_stats, bool), (
+        "Field 'print_stats' should be either 'true' or 'false'"
+    )
+
+    args = GenConvArgs(
+        num_conversations=conf["num_conversations"],
+        text_files=text_files,
+        input_num_turns=input_num_turns,
+        input_common_prefix_num_tokens=input_common_prefix_num_tokens,
+        input_prefix_num_tokens=input_prefix_num_tokens,
+        input_num_tokens=input_num_tokens,
+        output_num_tokens=output_num_tokens,
+        print_stats=print_stats,
+    )
+    return args
+
+
+def print_conv_stats(conversations: ConversationsMap, tokenizer: AutoTokenizer) -> None:
+    # Collect statistics
+    conv_stats: list[dict[Any, Any]] = []
+    req_stats: list[int] = []
+
+    print("\nCollecting statistics...")
+    for messages in conversations.values():
+        # messages is a list of dicts
+        user_tokens: list[int] = []
+        assistant_tokens: list[int] = []
+        request_tokens: list[int] = []
+
+        req_tokens = 0
+        for m in messages:
+            content = m["content"]
+            num_tokens = len(tokenizer(content).input_ids)
+
+            if m["role"] == "user":
+                user_tokens.append(num_tokens)
+                # New user prompt including all chat history
+                req_tokens += num_tokens
+                request_tokens.append(req_tokens)
+
+            elif m["role"] == "assistant":
+                assistant_tokens.append(num_tokens)
+                # Update assistant answer
+                # (will be part of chat history for the next user prompt)
+                req_tokens += num_tokens
+
+        item_stats = {
+            "conversation_turns": len(messages),
+            "user_tokens": mean(user_tokens),
+            "assistant_tokens": mean(assistant_tokens),
+        }
+
+        conv_stats.append(item_stats)
+        req_stats.extend(request_tokens)
+
+    # Print statistics
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99]
+
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    df = pd.DataFrame(conv_stats)
+    print(df.describe(percentiles=percentiles).transpose())
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Request statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    df = pd.DataFrame(req_stats, columns=["request_tokens"])
+    print(df.describe(percentiles=percentiles).transpose())
+    print(TEXT_SEPARATOR)
+
+
+def generate_conversations(
+    args: GenConvArgs, tokenizer: AutoTokenizer
+) -> ConversationsMap:
+    # Text for all user prompts
+    # (text from the input text files will be appended to this line)
+    base_prompt_text = "Please rewrite the following text and add more content: "
+    base_prompt_token_count = len(
+        tokenizer.encode(base_prompt_text, add_special_tokens=False)
+    )
+
+    logger.info(f"{Color.PURPLE}Generating conversations...{Color.RESET}")
+    logger.info(args)
+
+    list_of_tokens = []
+
+    for filename in args.text_files:
+        # Load text file that will be used to generate prompts
+        with open(filename) as file:
+            data = file.read()
+            tokens_in_file = tokenizer.encode(data, add_special_tokens=False)
+            list_of_tokens.extend(tokens_in_file)
+
+    conversations: ConversationsMap = {}
+    conv_id = 0
+
+    # Generate number of turns for every conversation
+    turn_count: np.ndarray = args.input_num_turns.sample(args.num_conversations)
+
+    # Turn count should be at least 2 (one user prompt and one assistant answer)
+    turn_count = np.maximum(turn_count, 2)
+
+    # Round up to an even number (every user prompt should have an answer)
+    turn_count = turn_count + (turn_count % 2)
+
+    # Generate number of prefix tokens for every conversation
+    conv_prefix_tokens: np.ndarray = args.input_prefix_num_tokens.sample(
+        args.num_conversations
+    )
+
+    # Used to reduce shared text between conversations
+    # (jump/skip over text sections between conversations)
+    base_offset = 0
+
+    # Common prefix size for all conversations (only 1 sample required)
+    common_prefix_text = ""
+    common_prefix_tokens: int = args.input_common_prefix_num_tokens.sample(1)[0]
+    if common_prefix_tokens > 0:
+        # Using "." at the end to separate sentences
+        common_prefix_text = (
+            tokenizer.decode(list_of_tokens[: common_prefix_tokens - 2]) + "."
+        )
+        base_offset += common_prefix_tokens
+
+    for conv_id in range(args.num_conversations):
+        # Generate a single conversation
+        messages: MessagesList = []
+
+        nturns = turn_count[conv_id]
+
+        # User prompt token count per turn (with lower limit)
+        input_token_count: np.ndarray = args.input_num_tokens.sample(nturns)
+        input_token_count = np.maximum(input_token_count, base_prompt_token_count)
+
+        # Assistant answer token count per turn (with lower limit)
+        output_token_count: np.ndarray = args.output_num_tokens.sample(nturns)
+        output_token_count = np.maximum(output_token_count, 1)
+
+        user_turn = True
+        for turn_id in range(nturns):
+            if user_turn:
+                role = "user"
+                num_tokens = input_token_count[turn_id]
+
+                # Generate the user prompt,
+                # use a unique prefix (the conv_id) for each conversation
+                # (to avoid shared prefix between conversations)
+                content = f"{conv_id} is a nice number... "
+
+                if len(common_prefix_text) > 0 and turn_id == 0:
+                    content = common_prefix_text + content
+
+                # Update the number of tokens left for the content
+                num_tokens -= len(tokenizer.encode(content, add_special_tokens=False))
+
+                if turn_id == 0:
+                    prefix_num_tokens = conv_prefix_tokens[conv_id]
+                    if prefix_num_tokens > 0:
+                        # Add prefix text (context) to the first turn
+                        start_offset = base_offset
+                        end_offset = start_offset + prefix_num_tokens
+                        assert len(list_of_tokens) > end_offset, (
+                            "Not enough input text to generate "
+                            f"{prefix_num_tokens} tokens for the "
+                            f"prefix text ({start_offset=}, {end_offset=})"
+                        )
+
+                        content += f"{conv_id}, " + tokenizer.decode(
+                            list_of_tokens[start_offset:end_offset]
+                        )
+                        base_offset += prefix_num_tokens
+
+                # Add the actual user prompt/question after the prefix text
+                content += base_prompt_text
+                num_tokens -= base_prompt_token_count
+
+                if num_tokens > 0:
+                    # Add text from the input file (to reach the desired token count)
+                    start_offset = base_offset + turn_id * input_token_count.max()
+                    end_offset = start_offset + num_tokens
+                    assert len(list_of_tokens) > end_offset, (
+                        f"Not enough input text to generate {num_tokens} tokens "
+                        f"for the prompt ({start_offset=}, {end_offset=})"
+                    )
+
+                    # Convert tokens back to text
+                    content += tokenizer.decode(list_of_tokens[start_offset:end_offset])
+            else:
+                role = "assistant"
+                # This content will not be used as input to the LLM server
+                # (actual answers will be used instead).
+                # Content is only required to determine the min_tokens/max_tokens
+                # (inputs to the LLM server).
+                num_tokens = output_token_count[turn_id]
+                assert len(list_of_tokens) > num_tokens, (
+                    f"Not enough input text to generate {num_tokens} "
+                    "tokens for assistant content"
+                )
+                content = tokenizer.decode(list_of_tokens[:num_tokens])
+
+            # Append the user/assistant message to the list of messages
+            messages.append({"role": role, "content": content})
+            user_turn = not user_turn
+
+        # Add the new conversation
+        conversations[f"CONV_ID_{conv_id}"] = messages
+
+        # Increase base offset for the next conversation
+        base_offset += nturns
+
+    if args.print_stats:
+        print_conv_stats(conversations, tokenizer)
+
+    return conversations
+
+
+def conversations_list_to_dict(input_list: ShareGptConversations) -> ConversationsMap:
+    conversations: ConversationsMap = {}
+
+    for item in input_list:
+        conv_id: str = item["id"]
+        assert isinstance(conv_id, str)
+
+        assert conv_id not in conversations, (
+            f"Conversation ID {conv_id} found more than once in the input"
+        )
+
+        messages: MessagesList = item["messages"]
+        assert isinstance(messages, list), (
+            f"Conversation messages should be a list (ID: {conv_id})"
+        )
+        assert len(messages) > 0, f"Conversation with no messages (ID: {conv_id})"
+
+        conversations[conv_id] = messages
+
+    logger.info(f"Using {len(conversations)} unique conversations (IDs)")
+    assert len(conversations) == len(input_list)
+
+    # Print statistics about the selected conversations
+    stats: list[dict[str, Any]] = []
+    for conv_data in conversations.values():
+        stats.append({"num_turns": len(conv_data)})
+
+    print(TEXT_SEPARATOR)
+    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
+    print(TEXT_SEPARATOR)
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
+    conv_stats = pd.DataFrame(stats).describe(percentiles=percentiles)
+    print(conv_stats.transpose())
+    print(TEXT_SEPARATOR)
+
+    return conversations
+
+
+def conversations_dict_to_list(input_dict: ConversationsMap) -> ShareGptConversations:
+    output: ShareGptConversations = []
+    for conv_id, conv_data in input_dict.items():
+        new_item = {"id": conv_id, "messages": conv_data}
+        output.append(new_item)
+
+    return output
--- a/benchmarks/multi_turn/bench_utils.py
+++ b/benchmarks/multi_turn/bench_utils.py
@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import logging
+from enum import Enum
+
+
+class Color(Enum):
+    RED = "\033[91m"
+    GREEN = "\033[92m"
+    BLUE = "\033[94m"
+    PURPLE = "\033[95m"
+    CYAN = "\033[96m"
+    YELLOW = "\033[93m"
+    RESET = "\033[0m"
+
+    def __str__(self):
+        return self.value
+
+
+TEXT_SEPARATOR = "-" * 100
+
+# Configure the logger
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] - %(message)s",
+    datefmt="%d-%m-%Y %H:%M:%S",
+)
+logger = logging.getLogger(__name__)
--- a/benchmarks/multi_turn/benchmark_serving_multi_turn.py
+++ b/benchmarks/multi_turn/benchmark_serving_multi_turn.py
--- a/benchmarks/multi_turn/convert_sharegpt_to_openai.py
+++ b/benchmarks/multi_turn/convert_sharegpt_to_openai.py
@ -0,0 +1,354 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Download dataset from:
+https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json
+
+Convert to OpenAI API:
+export INPUT_FILE=sharegpt_20230401_clean_lang_split.json
+python convert_sharegpt_to_openai.py $INPUT_FILE sharegpt_conv_128.json --max-items=128
+"""
+
+import argparse
+import json
+import random
+from statistics import mean
+from typing import Any, Optional
+
+import pandas as pd  # type: ignore
+import tqdm  # type: ignore
+from transformers import AutoTokenizer  # type: ignore
+
+
+def has_non_english_chars(text: str) -> bool:
+    return not text.isascii()
+
+
+def content_is_valid(
+    content: str, min_content_len: Optional[int], max_content_len: Optional[int]
+) -> bool:
+    if min_content_len and len(content) < min_content_len:
+        return False
+
+    if max_content_len and len(content) > max_content_len:
+        return False
+
+    return has_non_english_chars(content)
+
+
+def print_stats(
+    conversations: "list[dict[Any, Any]]", tokenizer: Optional[AutoTokenizer] = None
+) -> None:
+    # Collect statistics
+    stats = []
+
+    print("\nCollecting statistics...")
+    for item in tqdm.tqdm(conversations):
+        # item has "id" and "messages"
+        messages = item["messages"]
+
+        user_turns = 0
+        assistant_turns = 0
+        user_words = 0
+        assistant_words = 0
+        conv_chars = 0
+
+        user_tokens: list[int] = []
+        assistant_tokens: list[int] = []
+
+        for m in messages:
+            content = m["content"]
+            conv_chars += len(content)
+            content_num_words = content.count(" ") + 1
+
+            num_tokens = 0
+            if tokenizer:
+                num_tokens = len(tokenizer(m["content"]).input_ids)
+
+            if m["role"] == "user":
+                user_turns += 1
+                user_words += content_num_words
+                if tokenizer:
+                    user_tokens.append(num_tokens)
+
+            elif m["role"] == "assistant":
+                assistant_turns += 1
+                assistant_words += content_num_words
+                if tokenizer:
+                    assistant_tokens.append(num_tokens)
+
+        # assert user_turns == assistant_turns, \
+        # f"Invalid conversation ID {item['id']}"
+
+        conv_words = user_words + assistant_words
+        item_stats = {
+            "user_turns": user_turns,
+            "assistant_turns": assistant_turns,
+            "user_words": user_words,
+            "assistant_words": assistant_words,
+            "conv_turns": len(messages),
+            "conv_words": conv_words,
+            "conv_characters": conv_chars,
+        }
+
+        if len(user_tokens) > 0:
+            item_stats["user_tokens"] = int(mean(user_tokens))
+
+        if len(assistant_tokens) > 0:
+            item_stats["assistant_tokens"] = int(mean(assistant_tokens))
+
+        stats.append(item_stats)
+
+    print("\nStatistics:")
+    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
+    df = pd.DataFrame(stats)
+    print(df.describe(percentiles=percentiles).transpose())
+
+
+def convert_sharegpt_to_openai(
+    seed: int,
+    input_file: str,
+    output_file: str,
+    max_items: Optional[int],
+    min_content_len: Optional[int] = None,
+    max_content_len: Optional[int] = None,
+    min_turns: Optional[int] = None,
+    max_turns: Optional[int] = None,
+    model: Optional[str] = None,
+) -> None:
+    if min_turns and max_turns:
+        assert min_turns <= max_turns
+
+    if min_content_len and max_content_len:
+        # Verify that min is not larger than max if both were given
+        assert min_content_len <= max_content_len
+
+    print(
+        f"Input parameters:\n{seed=}, {max_items=}, {min_content_len=},"
+        f" {max_content_len=}, {min_turns=}, {max_turns=}\n"
+    )
+
+    random.seed(seed)
+
+    tokenizer = None
+    if model is not None:
+        print(f"Loading tokenizer from: {model}")
+        tokenizer = AutoTokenizer.from_pretrained(model)
+
+    # Read the ShareGPT JSON file
+    print(f"Reading file: {input_file}")
+    with open(input_file, encoding="utf-8") as f:
+        # Should be a list of dicts
+        # Each dict should have "id" (string) and "conversations" (list of dicts)
+        sharegpt_data = json.load(f)
+
+    assert isinstance(sharegpt_data, list), "Input file should contain a list of dicts"
+
+    print(f"Total items in input file: {len(sharegpt_data):,}")
+
+    print(f"Shuffling dataset with seed {seed}")
+    random.shuffle(sharegpt_data)
+
+    # Map conversation ID to the all the messages
+    conversation_parts: dict[str, list[Any]] = {}
+
+    for item in tqdm.tqdm(sharegpt_data):
+        assert "id" in item, "Missing key 'id'"
+        assert "conversations" in item, "Missing key 'conversations'"
+
+        # Conversation ID (e.g: "hiWPlMD") and part/session (0, 1, 2, etc.)
+        conv_id, _ = item["id"].split("_")
+        new_turns = item["conversations"]
+
+        if conv_id not in conversation_parts:
+            # Start new conversation
+            conversation_parts[conv_id] = []
+        elif len(conversation_parts[conv_id]) > 0 and len(new_turns) > 0:
+            prev_turns = conversation_parts[conv_id][-1]
+            if prev_turns[-1]["from"] == new_turns[0]["from"]:
+                new_turns = new_turns[1:]
+
+        if len(new_turns) > 0:
+            # We assume that parts are in order in the ShareGPT dataset
+            conversation_parts[conv_id].append(new_turns)
+
+    dataset: list[dict[str, Any]] = []
+    for conv_id, conv_parts in conversation_parts.items():
+        new_item = {"id": conv_id}
+
+        conversations: list[dict[str, str]] = []
+
+        # Merge all parts
+        for conv_part in conv_parts:
+            conversations.extend(conv_part)
+
+        if len(conversations) > 0:
+            new_item["conversations"] = conversations
+            dataset.append(new_item)
+
+    print(f"Total unique conversations (IDs) in input file: {len(dataset):,}")
+
+    # Final output data
+    final_openai_dataset: list[dict] = []
+
+    # Filter conversations from the ShareGPT dataset and convert to OpenAI format
+    for item in tqdm.tqdm(dataset):
+        messages: list[dict] = []
+
+        assert "id" in item, "Missing key 'id'"
+        assert "conversations" in item, "Missing key 'conversations'"
+
+        conv_id = item["id"]
+        conversations = item["conversations"]
+
+        if min_turns is not None and len(conversations) < min_turns:
+            # Skip short conversations
+            continue
+
+        # Convert each message in the conversation, up to max_turns if specified
+        for i, turn in enumerate(conversations):
+            assert "from" in turn and "value" in turn, (
+                f"Invalid conversation ID {conv_id} - missing 'from' or 'value'"
+            )
+
+            role = None
+            turn_from = turn["from"]
+
+            if turn_from in {"human", "user"}:
+                role = "user"
+            elif turn_from in {"gpt", "bing", "chatgpt", "bard"}:
+                role = "assistant"
+            elif turn_from == "system":
+                role = "system"
+
+            assert role is not None, (
+                f"Invalid conversation ID {conv_id} - 'from'='{turn_from}' is invalid"
+            )
+
+            if i == 0 and role != "user":
+                # If the first message is from assistant (gpt), skip it.
+                # this happens when the conversation is a follow-up
+                # to a previous conversation (from the same user).
+                continue
+
+            if max_turns is not None and i >= max_turns:
+                break
+
+            # Convert message to OpenAI format (with "role" and "content")
+            content = turn["value"]
+            messages.append({"role": role, "content": content})
+
+        # Add the converted conversation to the OpenAI format
+        if len(messages) > 0:
+            valid_messages = True
+
+            # First turn should always be from the user
+            user_turn = True
+
+            for m in messages:
+                # Make sure that turns alternate between user and assistant
+                if (user_turn and m["role"] != "user") or (
+                    not user_turn and m["role"] != "assistant"
+                ):
+                    valid_messages = False
+                    break
+
+                user_turn = not user_turn
+
+                content = m["content"]
+                valid_messages = content_is_valid(
+                    content, min_content_len, max_content_len
+                )
+                if not valid_messages:
+                    break
+
+            if valid_messages is True:
+                final_openai_dataset.append({"id": conv_id, "messages": messages})
+
+    assert len(final_openai_dataset) > 0, "Final number of conversations is zero"
+
+    print_stats(final_openai_dataset)
+
+    print_stats_again = False
+    if max_items is not None and len(final_openai_dataset) > max_items:
+        print(f"\n\nSampling {max_items} items from the dataset...")
+        print_stats_again = True
+        final_openai_dataset = random.sample(final_openai_dataset, max_items)
+
+    if print_stats_again:
+        # Print stats after the dataset changed
+        print_stats(final_openai_dataset, tokenizer)
+
+    # Write the converted data to a new JSON file
+    final_size = len(final_openai_dataset)
+    print(f"\nTotal conversations converted (after filtering): {final_size:,}")
+    print(f"\nWriting file: {output_file}")
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(final_openai_dataset, f, ensure_ascii=False, indent=2)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert ShareGPT dataset to OpenAI API format"
+    )
+    parser.add_argument("input_file", help="Path to the input ShareGPT JSON file")
+    parser.add_argument(
+        "output_file", help="Path to the output OpenAI format JSON file"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=0, help="Seed for random number generators"
+    )
+    parser.add_argument(
+        "--max-items",
+        type=int,
+        default=None,
+        help="Maximum number of items in the output file",
+    )
+    parser.add_argument(
+        "--min-turns",
+        type=int,
+        default=None,
+        help="Minimum number of turns per conversation",
+    )
+    parser.add_argument(
+        "--max-turns",
+        type=int,
+        default=None,
+        help="Maximum number of turns per conversation",
+    )
+    parser.add_argument(
+        "--min-content-len",
+        type=int,
+        default=None,
+        help="Min number of characters in the messages' content",
+    )
+    parser.add_argument(
+        "--max-content-len",
+        type=int,
+        default=None,
+        help="Max number of characters in the messages' content",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="LLM model, only the tokenizer will be used",
+    )
+
+    args = parser.parse_args()
+
+    convert_sharegpt_to_openai(
+        args.seed,
+        args.input_file,
+        args.output_file,
+        args.max_items,
+        args.min_content_len,
+        args.max_content_len,
+        args.min_turns,
+        args.max_turns,
+        args.model,
+    )
+
+
+if __name__ == "__main__":
+    main()
--- a/benchmarks/multi_turn/generate_multi_turn.json
+++ b/benchmarks/multi_turn/generate_multi_turn.json
@ -0,0 +1,35 @@
+{
+    "filetype": "generate_conversations",
+    "num_conversations": 24,
+    "text_files": ["pg1184.txt"],
+    "print_stats": false,
+    "prompt_input": {
+        "num_turns": {
+            "distribution": "uniform",
+            "min": 12,
+            "max": 18
+        },
+        "common_prefix_num_tokens": {
+            "distribution": "constant",
+            "value": 500
+        },
+        "prefix_num_tokens": {
+            "distribution": "lognormal",
+            "mean": 6,
+            "sigma": 4,
+            "max": 1500
+        },
+        "num_tokens": {
+            "distribution": "uniform",
+            "min": 120,
+            "max": 160
+        }
+    },
+    "prompt_output": {
+        "num_tokens": {
+            "distribution": "uniform",
+            "min": 80,
+            "max": 120
+        }
+    }
+}
--- a/benchmarks/multi_turn/requirements.txt
+++ b/benchmarks/multi_turn/requirements.txt
@ -0,0 +1,5 @@
+numpy>=1.24
+pandas>=2.0.0
+aiohttp>=3.10
+transformers>=4.46
+xlsxwriter>=3.2.1
--- a/cmake/cpu_extension.cmake
+++ b/cmake/cpu_extension.cmake
@ -1,6 +1,7 @@
 include(FetchContent)

 set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_EXTENSIONS ON)
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)

@ -87,6 +88,7 @@ is_avx512_disabled(AVX512_DISABLED)

 if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
    message(STATUS "Apple Silicon Detected")
+    set(APPLE_SILICON_FOUND TRUE)
    set(ENABLE_NUMA OFF)
    check_sysctl(hw.optional.neon ASIMD_FOUND)
    check_sysctl(hw.optional.arm.FEAT_BF16 ARM_BF16_FOUND)
@ -182,17 +184,17 @@ endif()
 #
 # Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 /ARM platforms)
 # Flag to enable ACL kernels for AARCH64 platforms
-if ( VLLM_BUILD_ACL STREQUAL "ON")
+if (VLLM_BUILD_ACL STREQUAL "ON")
    set(USE_ACL ON)
 else()
    set(USE_ACL OFF)
 endif()

-if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
+if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
    FetchContent_Declare(
        oneDNN
        GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-        GIT_TAG  v3.8.1
+        GIT_TAG v3.9
        GIT_PROGRESS TRUE
        GIT_SHALLOW TRUE
    )
@ -204,7 +206,7 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
        endif()
        set(ONEDNN_AARCH64_USE_ACL "ON")
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
-        endif()
+    endif()

    set(ONEDNN_LIBRARY_TYPE "STATIC")
    set(ONEDNN_BUILD_DOC "OFF")
@ -217,38 +219,23 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
    set(ONEDNN_ENABLE_ITT_TASKS "OFF")
    set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
    set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
+    set(ONEDNN_VERBOSE "OFF")
    set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)

    FetchContent_MakeAvailable(oneDNN)
-    
-    list(APPEND LIBS dnnl)
-elseif(POWER10_FOUND)
-    FetchContent_Declare(
-        oneDNN
-        GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-        GIT_TAG v3.7.2
-        GIT_PROGRESS TRUE
-        GIT_SHALLOW TRUE
+    add_library(dnnl_ext OBJECT "csrc/cpu/dnnl_helper.cpp")
+    target_include_directories(
+        dnnl_ext
+        PUBLIC ${oneDNN_SOURCE_DIR}/include
+        PUBLIC ${oneDNN_BINARY_DIR}/include
+        PRIVATE ${oneDNN_SOURCE_DIR}/src
    )
-
-    set(ONEDNN_LIBRARY_TYPE "STATIC")
-    set(ONEDNN_BUILD_DOC "OFF")
-    set(ONEDNN_BUILD_EXAMPLES "OFF")
-    set(ONEDNN_BUILD_TESTS "OFF")
-    set(ONEDNN_ENABLE_WORKLOAD "INFERENCE")
-    set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
-    set(ONEDNN_BUILD_GRAPH "OFF")
-    set(ONEDNN_ENABLE_JIT_PROFILING "OFF")
-    set(ONEDNN_ENABLE_ITT_TASKS "OFF")
-    set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
-    set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
-    set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
-
-    set(DNNL_CPU_RUNTIME "OMP")
-
-    FetchContent_MakeAvailable(oneDNN)
-
-    list(APPEND LIBS dnnl)
+    target_link_libraries(dnnl_ext dnnl)
+    target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
+    list(APPEND LIBS dnnl_ext)
+    set(USE_ONEDNN ON)
+else()
+    set(USE_ONEDNN OFF)
 endif()

 message(STATUS "CPU extension compile flags: ${CXX_COMPILE_FLAGS}")
@ -275,7 +262,6 @@ set(VLLM_EXT_SRC

 if (AVX512_FOUND AND NOT AVX512_DISABLED)
    set(VLLM_EXT_SRC
-        "csrc/cpu/quant.cpp"
        "csrc/cpu/shm.cpp"
        ${VLLM_EXT_SRC})
    if (ENABLE_AVX512BF16 AND ENABLE_AVX512VNNI)
@ -289,14 +275,11 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
            ${VLLM_EXT_SRC})
        add_compile_definitions(-DCPU_CAPABILITY_AVX512)
    endif()
-elseif(POWER10_FOUND)
-    set(VLLM_EXT_SRC
-        "csrc/cpu/quant.cpp"
-        ${VLLM_EXT_SRC})
 endif()
-if (ASIMD_FOUND)
+
+if(USE_ONEDNN)
    set(VLLM_EXT_SRC
-        "csrc/cpu/quant.cpp"
+        "csrc/cpu/dnnl_kernels.cpp"
        ${VLLM_EXT_SRC})
 endif()

--- a/cmake/external_projects/flashmla.cmake
+++ b/cmake/external_projects/flashmla.cmake
@ -19,7 +19,7 @@ else()
  FetchContent_Declare(
        flashmla
        GIT_REPOSITORY https://github.com/vllm-project/FlashMLA.git
-        GIT_TAG 575f7724b9762f265bbee5889df9c7d630801845
+        GIT_TAG a757314c04eedd166e329e846c820eb1bdd702de
        GIT_PROGRESS TRUE
        CONFIGURE_COMMAND ""
        BUILD_COMMAND ""
@ -37,13 +37,14 @@ cuda_archs_loose_intersection(FLASH_MLA_ARCHS "9.0a" "${CUDA_ARCHS}")
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3 AND FLASH_MLA_ARCHS)
    set(FlashMLA_SOURCES
        ${flashmla_SOURCE_DIR}/csrc/flash_api.cpp
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_bf16_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_fp16_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_metadata.cu)
+        ${flashmla_SOURCE_DIR}/csrc/kernels/get_mla_metadata.cu
+        ${flashmla_SOURCE_DIR}/csrc/kernels/mla_combine.cu
+        ${flashmla_SOURCE_DIR}/csrc/kernels/splitkv_mla.cu
+        ${flashmla_SOURCE_DIR}/csrc/kernels_fp8/flash_fwd_mla_fp8_sm90.cu)

    set(FlashMLA_INCLUDES
        ${flashmla_SOURCE_DIR}/csrc/cutlass/include
-        ${flashmla_SOURCE_DIR}/csrc/include)
+        ${flashmla_SOURCE_DIR}/csrc)

    set_gencode_flags_for_srcs(
        SRCS "${FlashMLA_SOURCES}"
--- a/cmake/external_projects/vllm_flash_attn.cmake
+++ b/cmake/external_projects/vllm_flash_attn.cmake
@ -38,7 +38,7 @@ else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 1c2624e53c078854e0637ee566c72fe2107e75f4
+          GIT_TAG ee4d25bd84e0cbc7e0b9b9685085fd5db2dcb62a
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
--- a/cmake/utils.cmake
+++ b/cmake/utils.cmake
@ -467,6 +467,12 @@ function (define_gpu_extension_target GPU_MOD_NAME)
  if (GPU_LANGUAGE STREQUAL "HIP")
    # Make this target dependent on the hipify preprocessor step.
    add_dependencies(${GPU_MOD_NAME} hipify${GPU_MOD_NAME})
+    # Make sure we include the hipified versions of the headers, and avoid conflicts with the ones in the original source folder
+    target_include_directories(${GPU_MOD_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/csrc
+      ${GPU_INCLUDE_DIRECTORIES})
+  else()
+    target_include_directories(${GPU_MOD_NAME} PRIVATE csrc
+      ${GPU_INCLUDE_DIRECTORIES})
  endif()

  if (GPU_ARCHITECTURES)
@ -482,8 +488,6 @@ function (define_gpu_extension_target GPU_MOD_NAME)
  target_compile_definitions(${GPU_MOD_NAME} PRIVATE
    "-DTORCH_EXTENSION_NAME=${GPU_MOD_NAME}")

-  target_include_directories(${GPU_MOD_NAME} PRIVATE csrc
-    ${GPU_INCLUDE_DIRECTORIES})

  target_link_libraries(${GPU_MOD_NAME} PRIVATE torch ${GPU_LIBRARIES})

--- a/csrc/activation_kernels.cu
+++ b/csrc/activation_kernels.cu
@ -128,6 +128,45 @@ __global__ void act_and_mul_kernel_with_param(
  }
 }

+template <typename T>
+__device__ __forceinline__ T swigluoai_and_mul(const T& gate, const T& up,
+                                               float alpha, float limit) {
+  // clamp gate: min=None, max=limit
+  const float gate_f = (float)gate;
+  const float clamped_gate = gate_f > limit ? limit : gate_f;
+
+  // clamp up: min=-limit, max=limit
+  const float up_f = (float)up;
+  const float clamped_up =
+      up_f > limit ? limit : (up_f < -limit ? -limit : up_f);
+
+  // glu = gate * sigmoid(gate * alpha)
+  const float sigmoid_val = 1.0f / (1.0f + expf(-clamped_gate * alpha));
+  const float glu = clamped_gate * sigmoid_val;
+
+  // (up + 1) * glu
+  return (T)((clamped_up + 1.0f) * glu);
+}
+
+template <typename scalar_t,
+          scalar_t (*ACT_FN)(const scalar_t&, const scalar_t&, const float,
+                             const float)>
+__global__ void swigluoai_and_mul_kernel(
+    scalar_t* __restrict__ out,          // [..., d]
+    const scalar_t* __restrict__ input,  // [..., 2, d]
+    const int d, const float alpha, const float limit) {
+  const int64_t token_idx = blockIdx.x;
+  // TODO: Vectorize loads and stores.
+  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
+    // gate = x[..., ::2]  (even indices)
+    const scalar_t gate = VLLM_LDG(&input[token_idx * 2 * d + 2 * idx]);
+    // up = x[..., 1::2]   (odd indices)
+    const scalar_t up = VLLM_LDG(&input[token_idx * 2 * d + 2 * idx + 1]);
+
+    out[token_idx * d + idx] = ACT_FN(gate, up, alpha, limit);
+  }
+}
+
 }  // namespace vllm

 #define LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(KERNEL, PARAM)         \
@ -145,11 +184,31 @@ __global__ void act_and_mul_kernel_with_param(
                                         PARAM);                        \
      });

+#define LAUNCH_SIGLUOAI_AND_MUL(KERNEL, ALPHA, LIMIT)                          \
+  int d = input.size(-1) / 2;                                                  \
+  int64_t num_tokens = input.numel() / input.size(-1);                         \
+  dim3 grid(num_tokens);                                                       \
+  dim3 block(std::min(d, 1024));                                               \
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                \
+      input.scalar_type(), "clamp_swiglu_kernel_with_params", [&] {            \
+        vllm::swigluoai_and_mul_kernel<scalar_t, KERNEL<scalar_t>>             \
+            <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),             \
+                                         input.data_ptr<scalar_t>(), d, ALPHA, \
+                                         LIMIT);                               \
+      });
+
 void fatrelu_and_mul(torch::Tensor& out,    // [..., d],
                     torch::Tensor& input,  // [..., 2 * d]
                     double threshold) {
  LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(vllm::fatrelu_kernel, threshold);
 }
+void swigluoai_and_mul(torch::Tensor& out,    // [..., d]
+                       torch::Tensor& input,  // [..., 2 * d]
+                       double alpha, double limit) {
+  LAUNCH_SIGLUOAI_AND_MUL(vllm::swigluoai_and_mul, alpha, limit);
+}
 namespace vllm {

 // Element-wise activation kernel template.
--- a/csrc/attention/mla/sm100_cutlass_mla_kernel.cu
+++ b/csrc/attention/mla/sm100_cutlass_mla_kernel.cu
@ -36,6 +36,7 @@ limitations under the License.
 #if !defined(CUDA_VERSION) || CUDA_VERSION < 12040
 void sm100_cutlass_mla_decode(
    torch::Tensor const& out,
+    torch::Tensor const& lse,
    torch::Tensor const& q_nope,
    torch::Tensor const& q_pe,
    torch::Tensor const& kv_c_and_k_pe_cache,
@ -64,11 +65,11 @@ struct IsPersistent {
  static const bool value = v;
 };

-template <typename T, bool IsPaged128, typename PersistenceOption = IsPersistent<true>>
+template <typename T, typename TOut, bool IsPaged128, typename PersistenceOption = IsPersistent<true>>
 struct MlaSm100 {
  using Element = T;
  using ElementAcc = float;
-  using ElementOut = T;
+  using ElementOut = TOut;

  using TileShape = Shape<_128, _128, Shape<_512, _64>>;
  using TileShapeH = cute::tuple_element_t<0, TileShape>;
@ -99,6 +100,7 @@ struct MlaSm100 {
 template <typename T>
 typename T::Fmha::Arguments args_from_options(
    at::Tensor const& out,
+    at::Tensor const& lse,
    at::Tensor const& q_nope,
    at::Tensor const& q_pe,
    at::Tensor const& kv_c_and_k_pe_cache,
@ -162,12 +164,15 @@ typename T::Fmha::Arguments args_from_options(
       stride_PT,
       page_count_total,
       page_size},
-      {static_cast<ElementOut*>(out.data_ptr()), stride_O, static_cast<ElementAcc*>(nullptr), stride_LSE},
+      {static_cast<ElementOut*>(out.data_ptr()),
+       stride_O,
+       static_cast<ElementAcc*>(lse.defined() ? lse.data_ptr() : nullptr),
+       stride_LSE},
      hw_info,
      // TODO(trevor-m): Change split_kv back to -1 when
      // https://github.com/NVIDIA/cutlass/issues/2274 is fixed. Split_kv=1 will
      // perform worse with larger context length and smaller batch sizes.
-      num_kv_splits, // split_kv
+      static_cast<int>(num_kv_splits), // split_kv
      nullptr,       // is_var_split_kv
  };
  // TODO(kaixih@nvidia): When split_kv=-1 and is_var_split_kv=false, we compute
@ -178,9 +183,10 @@ typename T::Fmha::Arguments args_from_options(
  return arguments;
 }

-template <typename Element, bool IsPaged128, typename PersistenceOption>
+template <typename Element, typename ElementOut, bool IsPaged128, typename PersistenceOption>
 void runMla(
    at::Tensor const& out,
+    at::Tensor const& lse,
    at::Tensor const& q_nope,
    at::Tensor const& q_pe,
    at::Tensor const& kv_c_and_k_pe_cache,
@ -190,9 +196,9 @@ void runMla(
    double sm_scale,
    int64_t num_kv_splits,
    cudaStream_t stream) {
-  using MlaSm100Type = MlaSm100<Element, IsPaged128, PersistenceOption>;
+  using MlaSm100Type = MlaSm100<Element, ElementOut, IsPaged128, PersistenceOption>;
  typename MlaSm100Type::Fmha fmha;
-  auto arguments = args_from_options<MlaSm100Type>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, sm_scale, num_kv_splits);
+  auto arguments = args_from_options<MlaSm100Type>(out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, sm_scale, num_kv_splits);

  CUTLASS_CHECK(fmha.can_implement(arguments));

@ -214,6 +220,7 @@ void runMla(

 void sm100_cutlass_mla_decode(
    torch::Tensor const& out,
+    torch::Tensor const& lse,
    torch::Tensor const& q_nope,
    torch::Tensor const& q_pe,
    torch::Tensor const& kv_c_and_k_pe_cache,
@ -233,14 +240,14 @@ void sm100_cutlass_mla_decode(
  DISPATCH_BOOL(page_size == 128, IsPaged128, [&] {
    DISPATCH_BOOL(num_kv_splits <= 1, NotManualSplitKV, [&] {
      if (in_dtype == at::ScalarType::Half) {
-        runMla<cutlass::half_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+        runMla<cutlass::half_t, cutlass::half_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
      } else if (in_dtype == at::ScalarType::BFloat16) {
-        runMla<cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+        runMla<cutlass::bfloat16_t, cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
      } else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
-        runMla<cutlass::float_e4m3_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
+        runMla<cutlass::float_e4m3_t, cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
+          out, lse, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
      } else {
        TORCH_CHECK(false, "Unsupported input data type of MLA");
      }
@ -253,7 +260,7 @@ void sm100_cutlass_mla_decode(
 int64_t sm100_cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_batches, int64_t sm_count, int64_t num_kv_splits) {
  // Workspace size depends on ElementAcc and ElementLSE (same as ElementAcc)
  // which are float, so Element type here doesn't matter.
-  using MlaSm100Type = MlaSm100<cutlass::half_t, true>;
+  using MlaSm100Type = MlaSm100<cutlass::half_t, cutlass::half_t, true>;

  // Get split kv. Requires problem shape and sm_count only.
  typename MlaSm100Type::Fmha::Arguments arguments;
@ -264,7 +271,7 @@ int64_t sm100_cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_ba
  // Assumes device 0 when getting sm_count.
  arguments.hw_info.sm_count =
      sm_count <= 0 ? cutlass::KernelHardwareInfo::query_device_multiprocessor_count(/*device_id=*/0) : sm_count;
-  arguments.split_kv = num_kv_splits;
+  arguments.split_kv = static_cast<int>(num_kv_splits);
  MlaSm100Type::Fmha::set_split_kv(arguments);

  return MlaSm100Type::Fmha::get_workspace_size(arguments);
--- a/csrc/cache.h
+++ b/csrc/cache.h
@ -40,9 +40,19 @@ void concat_and_cache_mla(torch::Tensor& kv_c, torch::Tensor& k_pe,
 void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
                 const double scale, const std::string& kv_cache_dtype);

-void gather_cache(
+void gather_and_maybe_dequant_cache(
    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
    torch::Tensor const& cu_seq_lens,  // [BATCH+1]
-    int64_t batch_size, std::optional<torch::Tensor> seq_starts = std::nullopt);
+    int64_t batch_size, const std::string& kv_cache_dtype,
+    torch::Tensor const& scale,
+    std::optional<torch::Tensor> seq_starts = std::nullopt);
+
+// TODO(hc): cp_gather_cache need support scaled kvcahe in the future.
+void cp_gather_cache(
+    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
+    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
+    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
+    torch::Tensor const& cu_seq_lens,  // [BATCH+1]
+    int64_t batch_size, std::optional<torch::Tensor> seq_starts = std::nullopt);
--- a/csrc/cache_kernels.cu
+++ b/csrc/cache_kernels.cu
@ -1,6 +1,7 @@
 #include <torch/all.h>
 #include <ATen/cuda/CUDAContext.h>
 #include <c10/cuda/CUDAGuard.h>
+#include <c10/cuda/CUDAException.h>

 #include "cuda_utils.h"
 #include "cuda_compat.h"
@ -624,9 +625,9 @@ void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
 namespace vllm {

 // grid is launched with dimensions (batch, num_splits)
-template <typename scalar_t>
-__global__ void gather_cache(
-    const scalar_t* __restrict__ src_cache,   // [NUM_BLOCKS, BLOCK_SIZE,
+template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
+__global__ void gather_and_maybe_dequant_cache(
+    const cache_t* __restrict__ src_cache,    // [NUM_BLOCKS, BLOCK_SIZE,
                                              // ENTRIES...]
    scalar_t* __restrict__ dst,               // [TOT_TOKENS, ENTRIES...]
    const int32_t* __restrict__ block_table,  // [BATCH, BLOCK_INDICES]
@ -634,6 +635,7 @@ __global__ void gather_cache(
    const int32_t block_size, const int32_t entry_size,
    const int64_t block_table_stride, const int64_t cache_block_stride,
    const int64_t cache_entry_stride, const int64_t dst_entry_stride,
+    const float* __restrict__ scale,
    const int32_t* __restrict__ seq_starts) {  // Optional: starting offsets per
                                               // batch

@ -675,10 +677,16 @@ __global__ void gather_cache(
    if (partial_block_size) full_blocks_end -= 1;
  }

-  auto copy_entry = [&](const scalar_t* __restrict__ _src,
+  auto copy_entry = [&](const cache_t* __restrict__ _src,
                        scalar_t* __restrict__ _dst) {
-    for (int i = threadIdx.x; i < entry_size; i += blockDim.x)
-      _dst[i] = _src[i];
+    for (int i = threadIdx.x; i < entry_size; i += blockDim.x) {
+      if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+        _dst[i] = static_cast<scalar_t>(_src[i]);
+      } else {
+        _dst[i] =
+            fp8::scaled_convert<scalar_t, cache_t, kv_dt>(_src[i], *scale);
+      }
+    }
  };

  for (int pid = split_start; pid < full_blocks_end; ++pid) {
@ -705,8 +713,144 @@ __global__ void gather_cache(
 }  // namespace vllm

 // Macro to dispatch the kernel based on the data type.
-#define CALL_GATHER_CACHE(CPY_DTYPE)                                    \
-  vllm::gather_cache<CPY_DTYPE><<<grid, block, 0, stream>>>(            \
+// SCALAR_T is the data type of the destination tensor.
+// CACHE_T is the stored data type of kv-cache.
+// KV_DTYPE is the real data type of kv-cache.
+#define CALL_GATHER_CACHE(SCALAR_T, CACHE_T, KV_DTYPE)                      \
+  vllm::gather_and_maybe_dequant_cache<SCALAR_T, CACHE_T, KV_DTYPE>         \
+      <<<grid, block, 0, stream>>>(                                         \
+          reinterpret_cast<CACHE_T*>(src_cache.data_ptr()),                 \
+          reinterpret_cast<SCALAR_T*>(dst.data_ptr()),                      \
+          block_table.data_ptr<int32_t>(), cu_seq_lens.data_ptr<int32_t>(), \
+          block_size, entry_size, block_table_stride, cache_block_stride,   \
+          cache_entry_stride, dst_entry_stride,                             \
+          reinterpret_cast<const float*>(scale.data_ptr()), seq_starts_ptr);
+
+// Gather sequences from the cache into the destination tensor.
+//  - cu_seq_lens contains the cumulative sequence lengths for each batch
+//  - block_table contains the cache block indices for each sequence
+//  - Optionally, seq_starts (if provided) offsets the starting block index by
+//  (seq_starts[bid] / page_size)
+void gather_and_maybe_dequant_cache(
+    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
+    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
+    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
+    torch::Tensor const& cu_seq_lens,  // [BATCH+1]
+    int64_t batch_size, const std::string& kv_cache_dtype,
+    torch::Tensor const& scale,
+    std::optional<torch::Tensor> seq_starts = std::nullopt) {
+  at::cuda::OptionalCUDAGuard device_guard(src_cache.device());
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  int32_t block_size = src_cache.size(1);
+  int32_t entry_size = src_cache.flatten(2, -1).size(2);
+
+  TORCH_CHECK(block_table.dtype() == torch::kInt32,
+              "block_table must be int32");
+  TORCH_CHECK(cu_seq_lens.dtype() == torch::kInt32,
+              "cu_seq_lens must be int32");
+  if (seq_starts.has_value()) {
+    TORCH_CHECK(seq_starts.value().dtype() == torch::kInt32,
+                "seq_starts must be int32");
+  }
+
+  TORCH_CHECK(src_cache.device() == dst.device(),
+              "src_cache and dst must be on the same device");
+  TORCH_CHECK(src_cache.device() == block_table.device(),
+              "src_cache and block_table must be on the same device");
+  TORCH_CHECK(src_cache.device() == cu_seq_lens.device(),
+              "src_cache and cu_seq_lens must be on the same device");
+  if (seq_starts.has_value()) {
+    TORCH_CHECK(src_cache.device() == seq_starts.value().device(),
+                "src_cache and seq_starts must be on the same device");
+  }
+
+  int64_t block_table_stride = block_table.stride(0);
+  int64_t cache_block_stride = src_cache.stride(0);
+  int64_t cache_entry_stride = src_cache.stride(1);
+  int64_t dst_entry_stride = dst.stride(0);
+
+  // Decide on the number of splits based on the batch size.
+  int num_splits = batch_size > 128 ? 2 : batch_size > 64 ? 4 : 16;
+  dim3 grid(batch_size, num_splits);
+  dim3 block(1024);
+
+  const int32_t* seq_starts_ptr =
+      seq_starts.has_value() ? seq_starts.value().data_ptr<int32_t>() : nullptr;
+
+  DISPATCH_BY_KV_CACHE_DTYPE(dst.dtype(), kv_cache_dtype, CALL_GATHER_CACHE);
+}
+
+namespace vllm {
+template <typename scalar_t>
+// Note(hc): The cp_gather_cache allows seq_starts to no longer be divisible by
+// block_size.
+__global__ void cp_gather_cache(
+    const scalar_t* __restrict__ src_cache,   // [NUM_BLOCKS, BLOCK_SIZE,
+                                              // ENTRY_SIZE]
+    scalar_t* __restrict__ dst,               // [TOT_TOKENS, ENTRY_SIZE]
+    const int32_t* __restrict__ block_table,  // [BATCH, BLOCK_INDICES]
+    const int32_t* __restrict__ cu_seq_lens,  // [BATCH+1]
+    const int32_t block_size, const int32_t entry_size,
+    const int64_t block_table_stride, const int64_t cache_block_stride,
+    const int64_t cache_entry_stride, const int64_t dst_entry_stride,
+    const int32_t* __restrict__ seq_starts  // Optional: starting offsets per
+                                            // batch
+) {
+  const int64_t bid = blockIdx.x;  // Batch ID
+  const int32_t num_splits = gridDim.y;
+  const int32_t split = blockIdx.y;
+  const int32_t seq_start = cu_seq_lens[bid];
+  const int32_t seq_end = cu_seq_lens[bid + 1];
+  const int32_t seq_len = seq_end - seq_start;
+  const int32_t tot_slots = seq_len;
+  const int32_t split_slots = cuda_utils::ceil_div(tot_slots, num_splits);
+
+  const int32_t split_start = split * split_slots;
+  const int32_t split_end = min((split + 1) * split_slots, tot_slots);
+
+  const bool is_active_split = (split_start < tot_slots);
+
+  if (!is_active_split) return;
+
+  // Adjust the pointer for the block_table for this batch.
+  // If seq_starts is provided, compute an offset based on it
+  const int32_t batch_offset = bid * block_table_stride;
+  int32_t offset = split_start;
+  if (seq_starts != nullptr) {
+    offset += seq_starts[bid];
+  }
+  int32_t offset_div = offset / block_size;
+  offset = offset % block_size;
+  const int32_t* batch_block_table = block_table + batch_offset;
+
+  // Adjust dst pointer based on the cumulative sequence lengths.
+  dst += seq_start * dst_entry_stride;
+
+  auto copy_entry = [&](const scalar_t* __restrict__ _src,
+                        scalar_t* __restrict__ _dst) {
+    for (int i = threadIdx.x; i < entry_size; i += blockDim.x)
+      _dst[i] = _src[i];
+  };
+
+  for (int pid = split_start; pid < split_end; ++pid) {
+    auto block_id = batch_block_table[offset_div];
+    auto block_start_ptr = src_cache + block_id * cache_block_stride;
+    auto block_dst_ptr = dst + pid * dst_entry_stride;
+    copy_entry(block_start_ptr + offset * cache_entry_stride, block_dst_ptr);
+    offset += 1;
+    // bump to next block
+    if (offset == block_size) {
+      offset_div += 1;
+      offset = 0;
+    }
+  }
+}
+}  // namespace vllm
+
+// Macro to dispatch the kernel based on the data type.
+#define CALL_CP_GATHER_CACHE(CPY_DTYPE)                                 \
+  vllm::cp_gather_cache<CPY_DTYPE><<<grid, block, 0, stream>>>(         \
      reinterpret_cast<CPY_DTYPE*>(src_cache.data_ptr()),               \
      reinterpret_cast<CPY_DTYPE*>(dst.data_ptr()),                     \
      block_table.data_ptr<int32_t>(), cu_seq_lens.data_ptr<int32_t>(), \
@ -716,9 +860,9 @@ __global__ void gather_cache(
 // Gather sequences from the cache into the destination tensor.
 //  - cu_seq_lens contains the cumulative sequence lengths for each batch
 //  - block_table contains the cache block indices for each sequence
-//  - Optionally, seq_starts (if provided) offsets the starting block index by
-//  (seq_starts[bid] / page_size)
-void gather_cache(
+//  - Optionally, seq_starts (if provided) offsets the starting slot index by
+//  seq_starts[bid]
+void cp_gather_cache(
    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
@ -769,11 +913,11 @@ void gather_cache(
      seq_starts.has_value() ? seq_starts.value().data_ptr<int32_t>() : nullptr;

  if (dtype_bits == 32) {
-    CALL_GATHER_CACHE(uint32_t);
+    CALL_CP_GATHER_CACHE(uint32_t);
  } else if (dtype_bits == 16) {
-    CALL_GATHER_CACHE(uint16_t);
+    CALL_CP_GATHER_CACHE(uint16_t);
  } else if (dtype_bits == 8) {
-    CALL_GATHER_CACHE(uint8_t);
+    CALL_CP_GATHER_CACHE(uint8_t);
  } else {
    TORCH_CHECK(false, "Unsupported data type width: ", dtype_bits);
  }
--- a/csrc/core/scalar_type.hpp
+++ b/csrc/core/scalar_type.hpp
@ -321,6 +321,8 @@ static inline constexpr auto kFE3M2f =
    ScalarType::float_(3, 2, true, ScalarType::NAN_NONE);
 static inline constexpr auto kFE4M3fn =
    ScalarType::float_(4, 3, true, ScalarType::NAN_EXTD_RANGE_MAX_MIN);
+static inline constexpr auto kFE8M0fnu =
+    ScalarType(8, 0, false, 0, true, ScalarType::NAN_EXTD_RANGE_MAX_MIN);
 static inline constexpr auto kFE5M2 = ScalarType::float_IEEE754(5, 2);
 static inline constexpr auto kFE8M7 = ScalarType::float_IEEE754(8, 7);
 static inline constexpr auto kFE5M10 = ScalarType::float_IEEE754(5, 10);
--- a/csrc/cpu/cpu_types_x86.hpp
+++ b/csrc/cpu/cpu_types_x86.hpp
@ -89,7 +89,7 @@ struct FP16Vec16 : public Vec<FP16Vec16> {

  explicit FP16Vec16(const FP32Vec16&);

-  void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
+  void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }

  void save(void* ptr, const int elem_num) const {
    constexpr uint32_t M = 0xFFFFFFFF;
@ -126,7 +126,7 @@ struct BF16Vec16 : public Vec<BF16Vec16> {

  explicit BF16Vec16(const FP32Vec16&);

-  void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
+  void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }

  void save(void* ptr, const int elem_num) const {
    constexpr uint32_t M = 0xFFFFFFFF;
@ -180,8 +180,8 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
            (__m128i)vec8_data.reg, 1)) {}

  void save(void* ptr) const {
-    *reinterpret_cast<__m256i*>(ptr) = reg_low;
-    *reinterpret_cast<__m256i*>((__m256i*)ptr + 1) = reg_high;
+    _mm256_storeu_si256((__m256i*)ptr, reg_low);
+    _mm256_storeu_si256((__m256i*)ptr + 1, reg_high);
  }
 };
 #endif
--- a/csrc/cpu/dnnl_helper.cpp
+++ b/csrc/cpu/dnnl_helper.cpp
@ -0,0 +1,523 @@
+#include <list>
+#include <optional>
+
+#include "common/memory_desc.hpp"
+#include "common/memory.hpp"
+
+#include "dnnl_helper.h"
+
+static dnnl::engine& default_engine() {
+  static dnnl::engine engine(dnnl::engine::kind::cpu, 0);
+  return engine;
+}
+
+static dnnl::stream& default_stream() {
+  static dnnl::stream stream(default_engine());
+  return stream;
+}
+
+void release_dnnl_matmul_handler(int64_t handler) {
+  DNNLMatMulPrimitiveHandler* ptr =
+      reinterpret_cast<DNNLMatMulPrimitiveHandler*>(handler);
+  delete ptr;
+}
+
+DNNLScratchPadManager::DNNLScratchPadManager() : size_(0), ptr_(nullptr) {
+  this->realloc(allocation_unit * 128);
+}
+
+void DNNLScratchPadManager::realloc(size_t new_size) {
+  new_size = round(new_size);
+  if (new_size > size_) {
+    ptr_ = std::aligned_alloc(64, new_size);
+    size_ = new_size;
+  }
+}
+
+DNNLScratchPadManager* DNNLScratchPadManager::get_dnnl_scratchpad_manager() {
+  static DNNLScratchPadManager manager;
+  return &manager;
+}
+
+template <typename KT, typename VT>
+class DNNLPrimitiveCache {
+ public:
+  using cache_value_t = std::pair<KT, VT>;
+  using result_value_t = VT;
+  using container_t = std::list<cache_value_t>;
+  using value_iterator_t = typename container_t::iterator;
+  using map_t = std::unordered_map<KT, value_iterator_t>;
+  using creator_t = VT (*)();
+
+ public:
+  DNNLPrimitiveCache(size_t capacity)
+      : capacity_(capacity),
+        values_(),
+        key_to_value_(std::min(256lu, capacity)) {
+    assert(capacity > 0);
+  }
+
+  template <typename F>
+  result_value_t get_or_create(const KT& key, F&& creator) {
+    std::optional<value_iterator_t> value = get_value(key);
+    if (value.has_value()) {
+      return value.value()->second;
+    } else {
+      return add_value({key, creator()})->second;
+    }
+  }
+
+  size_t size() const { return values_.size(); }
+
+ private:
+  void dump_data() {
+    std::stringstream ss;
+    ss << "table_id: " << std::hex << reinterpret_cast<size_t>(this) << std::dec
+       << "\n";
+    ss << "container: [";
+    for (auto&& iter : values_) {
+      ss << "(" << iter.first << ", " << std::hex
+         << reinterpret_cast<size_t>(iter.second.get()) << "), " << std::dec;
+    }
+    ss << "]\n";
+
+    ss << "map: [";
+    for (auto&& iter : key_to_value_) {
+      ss << "(" << iter.first << ", " << iter.second->first << ", " << std::hex
+         << reinterpret_cast<size_t>(iter.second->second.get()) << std::dec
+         << "), ";
+    }
+    ss << "]\n";
+    std::printf("%s\n", ss.str().c_str());
+  }
+
+  value_iterator_t add_value(cache_value_t&& new_value) {
+    if (size() == capacity_) {
+      cache_value_t& last_item = values_.back();
+      key_to_value_.erase(last_item.first);
+      values_.pop_back();
+    }
+
+    auto& added_value_ = values_.emplace_front(std::move(new_value));
+    key_to_value_.emplace(added_value_.first, values_.begin());
+    return values_.begin();
+  }
+
+  std::optional<value_iterator_t> get_value(const KT& key) {
+    if (key_to_value_.size() > 0 && key == values_.begin()->first) {
+      return values_.begin();
+    }
+
+    auto value_map_iterator = key_to_value_.find(key);
+    if (value_map_iterator != key_to_value_.end()) {
+      values_.splice(values_.begin(), values_, value_map_iterator->second);
+      return value_map_iterator->second;
+    } else {
+      return {};
+    }
+  }
+
+ private:
+  const size_t capacity_;
+  container_t values_;
+  map_t key_to_value_;
+};
+
+DNNLMatMulPrimitiveHandler::DNNLMatMulPrimitiveHandler(
+    const Args& args, dnnl::memory::data_type b_type)
+    : b_n_size_(args.b_n_size),
+      b_n_stride_(args.b_n_stride),
+      b_k_size_(args.b_k_size),
+      b_k_stride_(args.b_k_stride),
+      b_type_(b_type),
+      c_type_(args.c_type),
+      runtime_memory_ptrs_(8),
+      primitive_cache_size_(args.primitive_cache_size) {
+  assert(primitive_cache_size_ > 0);
+}
+
+void DNNLMatMulPrimitiveHandler::prepack_weight(
+    void* original_b_ptr, dnnl::memory::desc b_target_mem_desc) {
+  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
+                                   {b_k_stride_, b_n_stride_});
+  dnnl::memory original_weight(original_b_md, default_engine(), original_b_ptr);
+  dnnl::memory packed_weight(b_target_mem_desc, default_engine());
+  {
+    dnnl::reorder(original_weight, packed_weight)
+        .execute(default_stream(), original_weight, packed_weight);
+    default_stream().wait();
+  }
+  memory_cache_[DNNL_ARG_WEIGHTS] = packed_weight;
+  b_target_mem_desc_ = b_target_mem_desc;
+}
+
+void DNNLMatMulPrimitiveHandler::set_runtime_memory_ptr(
+    size_t index, dnnl_memory* memory_ptr) {
+  dnnl::impl::memory_storage_t* mem_storage_ptr = memory_ptr->memory_storage();
+  dnnl_memory_desc* mem_desc = const_cast<dnnl_memory_desc*>(memory_ptr->md());
+  runtime_memory_ptrs_[index] = {mem_storage_ptr, mem_desc};
+}
+
+std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>
+DNNLMatMulPrimitiveHandler::get_runtime_memory_ptr(size_t index) {
+  return runtime_memory_ptrs_[index];
+}
+
+namespace std {
+template <>
+struct hash<W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey> {
+  size_t operator()(
+      const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& val) const {
+    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size) ^
+           hash<int>()(static_cast<int>(val.a_qs)) ^
+           hash<int>()(static_cast<int>(val.b_qs)) ^ hash<bool>()(val.use_azp) ^
+           hash<int>()(static_cast<int>(val.c_type));
+  }
+};
+
+template <>
+struct hash<W8A8MatMulPrimitiveHandler::MSizeCacheKey> {
+  size_t operator()(
+      const W8A8MatMulPrimitiveHandler::MSizeCacheKey& val) const {
+    return hash<dnnl_dim_t>()(val.a_m_size) ^ hash<bool>()(val.use_bias) ^
+           hash<int>()(static_cast<int>(val.bias_type));
+  }
+};
+
+template <>
+struct hash<MatMulPrimitiveHandler::ClassMatmulCacheKey> {
+  size_t operator()(
+      const MatMulPrimitiveHandler::ClassMatmulCacheKey& val) const {
+    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size);
+  }
+};
+
+template <>
+struct hash<MatMulPrimitiveHandler::MSizeCacheKey> {
+  size_t operator()(const MatMulPrimitiveHandler::MSizeCacheKey& val) const {
+    return hash<dnnl_dim_t>()(val.a_m_size) ^
+           hash<dnnl_dim_t>()(val.a_m_stride) ^ hash<bool>()(val.use_bias) ^
+           hash<int>()(static_cast<int>(val.bias_type));
+  }
+};
+}  // namespace std
+
+bool operator==(const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& l,
+                const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& r) {
+  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size &&
+         l.a_qs == r.a_qs && l.b_qs == r.b_qs && l.use_azp == r.use_azp &&
+         l.c_type == r.c_type;
+}
+
+bool operator==(const W8A8MatMulPrimitiveHandler::MSizeCacheKey& l,
+                const W8A8MatMulPrimitiveHandler::MSizeCacheKey& r) {
+  return l.use_bias == r.use_bias && l.a_m_size == r.a_m_size &&
+         l.bias_type == r.bias_type;
+}
+
+bool operator==(const MatMulPrimitiveHandler::ClassMatmulCacheKey& l,
+                const MatMulPrimitiveHandler::ClassMatmulCacheKey& r) {
+  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size;
+}
+
+bool operator==(const MatMulPrimitiveHandler::MSizeCacheKey& l,
+                const MatMulPrimitiveHandler::MSizeCacheKey& r) {
+  return l.a_m_size == r.a_m_size && l.a_m_stride == r.a_m_stride &&
+         l.use_bias == r.use_bias && l.bias_type == r.bias_type;
+}
+
+static std::shared_ptr<W8A8MatMulPrimitiveHandler::MSizeCache>
+get_w8a8_class_primitive_cache(
+    const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& key,
+    int64_t cache_size) {
+  static W8A8MatMulPrimitiveHandler::ClassMatmulCache cache(128);
+  assert(cache_size > 0);
+  return cache.get_or_create(key, [&]() {
+    return std::make_shared<W8A8MatMulPrimitiveHandler::MSizeCache>(cache_size);
+  });
+}
+
+W8A8MatMulPrimitiveHandler::W8A8MatMulPrimitiveHandler(const Args& args)
+    : DNNLMatMulPrimitiveHandler(
+          static_cast<const DNNLMatMulPrimitiveHandler::Args&>(args),
+          dnnl::memory::data_type::s8),
+      use_azp_(args.use_a_zero_point),
+      a_qs_(args.a_quantization_strategy),
+      b_qs_(args.b_quantization_strategy),
+      m_size_cache_(nullptr) {
+  assert(a_qs_ != QuantizationStrategy::PER_OUTPUT_CHANNEL);
+  assert(b_qs_ != QuantizationStrategy::PER_TOKEN);
+  if (a_qs_ == QuantizationStrategy::PER_TOKEN) {
+    assert(!use_azp_);
+  };
+  prepack_weight(args.b_ptr,
+                 create_primitive_desc(
+                     MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
+                                   .use_bias = false,
+                                   .bias_type = dnnl::memory::data_type::undef},
+                     true)
+                     .weights_desc());
+  init_runtime_memory_cache(args);
+}
+
+void W8A8MatMulPrimitiveHandler::execute(ExecArgs& args) {
+  auto&& [a_storage, a_mem_desc] = get_runtime_memory_ptr(0);
+  auto&& [c_storage, c_mem_desc] = get_runtime_memory_ptr(1);
+  a_storage->set_data_handle((void*)args.a_ptr);
+  a_mem_desc->dims[0] = args.a_m_size;
+  c_storage->set_data_handle((void*)args.c_ptr);
+  c_mem_desc->dims[0] = args.a_m_size;
+
+  if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
+    auto&& [a_scale_storage, a_scale_mem_desc] = get_runtime_memory_ptr(2);
+    a_scale_storage->set_data_handle((void*)args.a_scales_ptr);
+  }
+  if (use_azp_) {
+    auto&& [a_zero_point_storage, a_zero_point_mem_desc] =
+        get_runtime_memory_ptr(3);
+    a_zero_point_storage->set_data_handle((void*)args.a_zero_points_ptr);
+  }
+
+  if (args.use_bias) {
+    auto&& [bias_storage, bias_mem_desc] = get_runtime_memory_ptr(4);
+    bias_storage->set_data_handle((void*)args.bias_ptr);
+  }
+
+  dnnl::matmul matmul = get_matmul_cache(args);
+
+  auto&& [scratchpad_storage, scratchpad_mem_desc] = get_runtime_memory_ptr(5);
+  scratchpad_storage->set_data_handle(
+      DNNLScratchPadManager::get_dnnl_scratchpad_manager()->get_data<void>());
+
+  matmul.execute(default_stream(), memory_cache_);
+  default_stream().wait();
+}
+
+dnnl::matmul W8A8MatMulPrimitiveHandler::get_matmul_cache(
+    const MSizeCacheKey& key) {
+  if (m_size_cache_.get() == nullptr) {
+    ClassMatmulCacheKey key = {.b_n_size = b_n_size_,
+                               .b_k_size = b_k_size_,
+                               .a_qs = a_qs_,
+                               .b_qs = b_qs_,
+                               .use_azp = use_azp_,
+                               .c_type = c_type_};
+    m_size_cache_ = get_w8a8_class_primitive_cache(key, primitive_cache_size_);
+  }
+
+  return m_size_cache_->get_or_create(key, [&]() {
+    dnnl::matmul::primitive_desc desc = this->create_primitive_desc(key, false);
+    auto manager = DNNLScratchPadManager::get_dnnl_scratchpad_manager();
+    manager->realloc(desc.scratchpad_desc().get_size());
+    return dnnl::matmul(desc);
+  });
+}
+
+void W8A8MatMulPrimitiveHandler::init_runtime_memory_cache(const Args& args) {
+  memory_cache_[DNNL_ARG_SRC] = dnnl::memory({{1, b_k_size_},
+                                              dnnl::memory::data_type::s8,
+                                              dnnl::memory::format_tag::ab},
+                                             default_engine(), nullptr);
+  set_runtime_memory_ptr(0, memory_cache_[DNNL_ARG_SRC].get());
+  memory_cache_[DNNL_ARG_DST] =
+      dnnl::memory({{1, b_n_size_}, c_type_, dnnl::memory::format_tag::ab},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(1, memory_cache_[DNNL_ARG_DST].get());
+
+  // For PER_TOKEN, scales will be applied in outside epilogue
+  if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
+    memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC] = dnnl::memory(
+        {{1}, dnnl::memory::data_type::f32, {1}}, default_engine(), nullptr);
+    set_runtime_memory_ptr(
+        2, memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC].get());
+    if (use_azp_) {
+      memory_cache_[DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC] = dnnl::memory(
+          {{1}, dnnl::memory::data_type::s32, {1}}, default_engine(), nullptr);
+      set_runtime_memory_ptr(
+          3, memory_cache_[DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC].get());
+    }
+  }
+
+  if (b_qs_ == QuantizationStrategy::PER_TENSOR) {
+    memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS] =
+        dnnl::memory({{1}, dnnl::memory::data_type::f32, {1}}, default_engine(),
+                     (void*)args.b_scales_ptr);
+  } else if (b_qs_ == QuantizationStrategy::PER_OUTPUT_CHANNEL) {
+    memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS] =
+        dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
+                     default_engine(), (void*)args.b_scales_ptr);
+  }
+
+  memory_cache_[DNNL_ARG_BIAS] =
+      dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(4, memory_cache_[DNNL_ARG_BIAS].get());
+
+  memory_cache_[DNNL_ARG_SCRATCHPAD] =
+      dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(5, memory_cache_[DNNL_ARG_SCRATCHPAD].get());
+}
+
+dnnl::matmul::primitive_desc W8A8MatMulPrimitiveHandler::create_primitive_desc(
+    const MSizeCacheKey& key, bool first_time) {
+  dnnl::memory::desc a_md({key.a_m_size, b_k_size_},
+                          dnnl::memory::data_type::s8,
+                          dnnl::memory::format_tag::ab);
+  dnnl::memory::desc b_md;
+  if (first_time) {
+    b_md =
+        dnnl::memory::desc({b_k_size_, b_n_size_}, dnnl::memory::data_type::s8,
+                           dnnl::memory::format_tag::any);
+  } else {
+    b_md = b_target_mem_desc_;
+  }
+  dnnl::memory::desc c_md({key.a_m_size, b_n_size_}, c_type_,
+                          dnnl::memory::format_tag::ab);
+
+  dnnl::primitive_attr attr;
+
+  attr.set_scratchpad_mode(dnnl::scratchpad_mode::user);
+
+  // For PER_TOKEN, scales will be applied in outside epilogue
+  if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
+    attr.set_scales_mask(DNNL_ARG_SRC, 0);
+    if (use_azp_) {
+      attr.set_zero_points_mask(DNNL_ARG_SRC, 0);
+    }
+  }
+
+  if (b_qs_ == QuantizationStrategy::PER_TENSOR) {
+    attr.set_scales_mask(DNNL_ARG_WEIGHTS, 0);
+  } else if (b_qs_ == QuantizationStrategy::PER_OUTPUT_CHANNEL) {
+    attr.set_scales_mask(DNNL_ARG_WEIGHTS, 2);
+  }
+
+  if (key.use_bias) {
+    // For PER_TOKEN, bias will be applied in epilogue
+    assert(a_qs_ == QuantizationStrategy::PER_TENSOR);
+    dnnl::memory::desc bias_md({1, b_n_size_}, key.bias_type, {b_n_size_, 1});
+    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, bias_md,
+                                        c_md, attr);
+  } else {
+    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
+                                        attr);
+  }
+}
+
+MatMulPrimitiveHandler::MatMulPrimitiveHandler(const Args& args)
+    : DNNLMatMulPrimitiveHandler(
+          static_cast<DNNLMatMulPrimitiveHandler::Args>(args), args.ab_type),
+      m_size_cache_(nullptr) {
+  assert(ab_type_ == dnnl::memory::data_type::f32 ||
+         ab_type_ == dnnl::memory::data_type::bf16 ||
+         ab_type_ == dnnl::memory::data_type::f16);
+  prepack_weight(args.b_ptr,
+                 create_primitive_desc(
+                     MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
+                                   .a_m_stride = DNNL_RUNTIME_DIM_VAL,
+                                   .use_bias = false,
+                                   .bias_type = dnnl::memory::data_type::undef},
+                     true)
+                     .weights_desc());
+  init_runtime_memory_cache(args);
+}
+
+static std::shared_ptr<MatMulPrimitiveHandler::MSizeCache>
+get_matul_class_primitive_cache(
+    const MatMulPrimitiveHandler::ClassMatmulCacheKey& key,
+    int64_t cache_size) {
+  static MatMulPrimitiveHandler::ClassMatmulCache cache(128);
+  assert(cache_size > 0);
+  return cache.get_or_create(key, [&]() {
+    return std::make_shared<MatMulPrimitiveHandler::MSizeCache>(cache_size);
+  });
+}
+
+void MatMulPrimitiveHandler::execute(ExecArgs& args) {
+  auto&& [a_storage, a_mem_desc] = get_runtime_memory_ptr(0);
+  auto&& [c_storage, c_mem_desc] = get_runtime_memory_ptr(1);
+  a_storage->set_data_handle((void*)args.a_ptr);
+  a_mem_desc->dims[0] = args.a_m_size;
+  a_mem_desc->format_desc.blocking.strides[0] = args.a_m_stride;
+  c_storage->set_data_handle((void*)args.c_ptr);
+  c_mem_desc->dims[0] = args.a_m_size;
+
+  if (args.use_bias) {
+    auto&& [bias_storage, bias_mem_desc] = get_runtime_memory_ptr(2);
+    bias_storage->set_data_handle((void*)args.bias_ptr);
+  }
+
+  dnnl::matmul matmul = get_matmul_cache(args);
+
+  auto&& [scratchpad_storage, scratchpad_mem_desc] = get_runtime_memory_ptr(3);
+  scratchpad_storage->set_data_handle(
+      DNNLScratchPadManager::get_dnnl_scratchpad_manager()->get_data<void>());
+
+  matmul.execute(default_stream(), memory_cache_);
+  default_stream().wait();
+}
+
+dnnl::matmul MatMulPrimitiveHandler::get_matmul_cache(
+    const MSizeCacheKey& key) {
+  if (m_size_cache_.get() == nullptr) {
+    ClassMatmulCacheKey key = {.b_n_size = b_n_size_, .b_k_size = b_k_size_};
+    m_size_cache_ = get_matul_class_primitive_cache(key, primitive_cache_size_);
+  }
+  return m_size_cache_->get_or_create(key, [&]() {
+    dnnl::matmul::primitive_desc desc = this->create_primitive_desc(key, false);
+    auto manager = DNNLScratchPadManager::get_dnnl_scratchpad_manager();
+    manager->realloc(desc.scratchpad_desc().get_size());
+    return dnnl::matmul(desc);
+  });
+}
+
+dnnl::matmul::primitive_desc MatMulPrimitiveHandler::create_primitive_desc(
+    const MSizeCacheKey& key, bool first_time) {
+  dnnl::memory::desc a_md;
+  dnnl::memory::desc b_md;
+  if (first_time) {
+    a_md = dnnl::memory::desc({key.a_m_size, b_k_size_}, b_type_,
+                              dnnl::memory::format_tag::ab);
+    b_md = dnnl::memory::desc({b_k_size_, b_n_size_}, b_type_,
+                              dnnl::memory::format_tag::any);
+  } else {
+    a_md = dnnl::memory::desc({key.a_m_size, b_k_size_}, b_type_,
+                              {key.a_m_stride, 1});
+    b_md = b_target_mem_desc_;
+  }
+  dnnl::memory::desc c_md({key.a_m_size, b_n_size_}, c_type_,
+                          dnnl::memory::format_tag::ab);
+
+  dnnl::primitive_attr attr;
+  attr.set_scratchpad_mode(dnnl::scratchpad_mode::user);
+
+  if (key.use_bias) {
+    dnnl::memory::desc bias_md({1, b_n_size_}, key.bias_type, {b_n_size_, 1});
+    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, bias_md,
+                                        c_md, attr);
+  } else {
+    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
+                                        attr);
+  }
+}
+
+void MatMulPrimitiveHandler::init_runtime_memory_cache(const Args& args) {
+  memory_cache_[DNNL_ARG_SRC] = dnnl::memory(
+      {{1, b_k_size_}, b_type_, {b_k_size_, 1}}, default_engine(), nullptr);
+  set_runtime_memory_ptr(0, memory_cache_[DNNL_ARG_SRC].get());
+  memory_cache_[DNNL_ARG_DST] =
+      dnnl::memory({{1, b_n_size_}, c_type_, dnnl::memory::format_tag::ab},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(1, memory_cache_[DNNL_ARG_DST].get());
+
+  memory_cache_[DNNL_ARG_BIAS] =
+      dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(2, memory_cache_[DNNL_ARG_BIAS].get());
+
+  memory_cache_[DNNL_ARG_SCRATCHPAD] =
+      dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
+                   default_engine(), nullptr);
+  set_runtime_memory_ptr(3, memory_cache_[DNNL_ARG_SCRATCHPAD].get());
+}
--- a/csrc/cpu/dnnl_helper.h
+++ b/csrc/cpu/dnnl_helper.h
@ -0,0 +1,243 @@
+#ifndef DNNL_HELPER_H
+#define DNNL_HELPER_H
+
+#include <optional>
+#include <cassert>
+
+#include "oneapi/dnnl/dnnl.hpp"
+
+namespace c10 {
+struct BFloat16;
+struct Half;
+}  // namespace c10
+
+namespace dnnl {
+namespace impl {
+struct memory_storage_t;
+struct matmul_pd_t;
+struct matmul_desc_t;
+}  // namespace impl
+}  // namespace dnnl
+struct dnnl_memory_desc;
+
+template <typename KT, typename VT>
+class DNNLPrimitiveCache;
+
+template <typename T>
+struct DNNLType {
+  static constexpr dnnl::memory::data_type type =
+      dnnl::memory::data_type::undef;
+};
+
+template <>
+struct DNNLType<int8_t> {
+  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s8;
+};
+
+template <>
+struct DNNLType<int32_t> {
+  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s32;
+};
+
+template <>
+struct DNNLType<float> {
+  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f32;
+};
+
+template <>
+struct DNNLType<c10::BFloat16> {
+  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::bf16;
+};
+
+template <>
+struct DNNLType<c10::Half> {
+  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f16;
+};
+
+template <typename T>
+constexpr inline dnnl::memory::data_type get_dnnl_type() {
+  return DNNLType<std::decay_t<T>>::type;
+}
+
+class DNNLScratchPadManager {
+ public:
+  static constexpr size_t allocation_unit = 4 * 1024 * 1024;  // 4KB
+
+  static DNNLScratchPadManager* get_dnnl_scratchpad_manager();
+
+  DNNLScratchPadManager();
+
+  template <typename T>
+  T* get_data() {
+    return reinterpret_cast<T*>(ptr_);
+  }
+
+  static size_t round(size_t size) {
+    return ((size + allocation_unit - 1) / allocation_unit) * allocation_unit;
+  }
+
+  void realloc(size_t new_size);
+
+ private:
+  size_t size_;
+  void* ptr_;
+};
+
+class DNNLMatMulPrimitiveHandler {
+ public:
+  virtual ~DNNLMatMulPrimitiveHandler() = default;
+
+ protected:
+  struct Args {
+    dnnl_dim_t b_n_size;
+    dnnl_dim_t b_n_stride;
+    dnnl_dim_t b_k_size;
+    dnnl_dim_t b_k_stride;
+    void* b_ptr;
+    dnnl::memory::data_type c_type;
+    size_t primitive_cache_size;
+  };
+
+ protected:
+  DNNLMatMulPrimitiveHandler(const Args& args, dnnl::memory::data_type b_type);
+
+  void prepack_weight(void* original_b_ptr,
+                      dnnl::memory::desc b_target_mem_desc);
+
+  void set_runtime_memory_ptr(size_t index, dnnl_memory* memory_ptr);
+
+  std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>
+  get_runtime_memory_ptr(size_t index);
+
+ protected:
+  const dnnl_dim_t b_n_size_;
+  const dnnl_dim_t b_n_stride_;
+  const dnnl_dim_t b_k_size_;
+  const dnnl_dim_t b_k_stride_;
+  dnnl::memory::data_type b_type_;
+  dnnl::memory::data_type c_type_;
+  std::unordered_map<int, dnnl::memory> memory_cache_;
+  std::vector<std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>>
+      runtime_memory_ptrs_;
+  dnnl::memory::desc b_target_mem_desc_;
+  int64_t primitive_cache_size_;
+};
+
+class W8A8MatMulPrimitiveHandler : public DNNLMatMulPrimitiveHandler {
+ public:
+  enum class QuantizationStrategy { PER_TOKEN, PER_TENSOR, PER_OUTPUT_CHANNEL };
+
+  struct Args : public DNNLMatMulPrimitiveHandler::Args {
+    bool use_a_zero_point;
+    QuantizationStrategy a_quantization_strategy;
+    QuantizationStrategy b_quantization_strategy;
+    float* b_scales_ptr;
+  };
+
+  struct ClassMatmulCacheKey {
+    dnnl_dim_t b_n_size;
+    dnnl_dim_t b_k_size;
+    QuantizationStrategy a_qs;
+    QuantizationStrategy b_qs;
+    bool use_azp;
+    dnnl::memory::data_type c_type;
+
+    friend bool operator==(const ClassMatmulCacheKey& l,
+                           const ClassMatmulCacheKey& r);
+  };
+
+  struct MSizeCacheKey {
+    dnnl_dim_t a_m_size;
+    bool use_bias;
+    dnnl::memory::data_type bias_type;
+
+    friend bool operator==(const MSizeCacheKey& l, const MSizeCacheKey& r);
+  };
+
+  using MSizeCache = DNNLPrimitiveCache<MSizeCacheKey, dnnl::matmul>;
+  using ClassMatmulCache =
+      DNNLPrimitiveCache<ClassMatmulCacheKey, std::shared_ptr<MSizeCache>>;
+
+  struct ExecArgs : public MSizeCacheKey {
+    const int8_t* a_ptr;
+    const float* a_scales_ptr;
+    const int32_t* a_zero_points_ptr;
+    const void* bias_ptr;
+    void* c_ptr;
+  };
+
+ public:
+  W8A8MatMulPrimitiveHandler(const Args& args);
+
+  QuantizationStrategy get_input_scale_strategy() const { return a_qs_; }
+
+  bool get_input_use_zero_point() const { return use_azp_; }
+
+  void execute(ExecArgs& args);
+
+ private:
+  dnnl::matmul::primitive_desc create_primitive_desc(const MSizeCacheKey& key,
+                                                     bool first_time);
+
+  void init_runtime_memory_cache(const Args& args);
+
+  dnnl::matmul get_matmul_cache(const MSizeCacheKey& key);
+
+ private:
+  const bool use_azp_;
+  const QuantizationStrategy a_qs_;
+  const QuantizationStrategy b_qs_;
+  std::shared_ptr<MSizeCache> m_size_cache_;
+};
+
+class MatMulPrimitiveHandler : public DNNLMatMulPrimitiveHandler {
+ public:
+  struct Args : public DNNLMatMulPrimitiveHandler::Args {
+    dnnl::memory::data_type ab_type;
+  };
+
+  struct ClassMatmulCacheKey {
+    dnnl_dim_t b_n_size;
+    dnnl_dim_t b_k_size;
+
+    friend bool operator==(const ClassMatmulCacheKey& l,
+                           const ClassMatmulCacheKey& r);
+  };
+
+  struct MSizeCacheKey {
+    dnnl_dim_t a_m_size;
+    dnnl_dim_t a_m_stride;
+    bool use_bias;
+    dnnl::memory::data_type bias_type;
+
+    friend bool operator==(const MSizeCacheKey& l, const MSizeCacheKey& r);
+  };
+
+  using MSizeCache = DNNLPrimitiveCache<MSizeCacheKey, dnnl::matmul>;
+  using ClassMatmulCache =
+      DNNLPrimitiveCache<ClassMatmulCacheKey, std::shared_ptr<MSizeCache>>;
+
+  struct ExecArgs : public MSizeCacheKey {
+    const void* a_ptr;
+    const void* bias_ptr;
+    void* c_ptr;
+  };
+
+ public:
+  MatMulPrimitiveHandler(const Args& args);
+
+  void execute(ExecArgs& args);
+
+ private:
+  dnnl::matmul::primitive_desc create_primitive_desc(const MSizeCacheKey& key,
+                                                     bool first_time);
+
+  void init_runtime_memory_cache(const Args& args);
+
+  dnnl::matmul get_matmul_cache(const MSizeCacheKey& key);
+
+ private:
+  std::shared_ptr<MSizeCache> m_size_cache_;
+};
+
+#endif
--- a/csrc/cpu/dnnl_helper.hpp
+++ b/csrc/cpu/dnnl_helper.hpp
@ -1,206 +0,0 @@
-#ifndef DNNL_HELPER_HPP
-#define DNNL_HELPER_HPP
-
-#include <c10/util/BFloat16.h>
-#include <c10/util/Half.h>
-
-#include "oneapi/dnnl/dnnl.hpp"
-
-namespace {
-template <typename T>
-struct DNNLType {
-  static constexpr dnnl::memory::data_type type =
-      dnnl::memory::data_type::undef;
-};
-
-template <>
-struct DNNLType<int8_t> {
-  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s8;
-};
-
-template <>
-struct DNNLType<int32_t> {
-  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s32;
-};
-
-template <>
-struct DNNLType<float> {
-  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f32;
-};
-
-template <>
-struct DNNLType<c10::BFloat16> {
-  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::bf16;
-};
-
-template <>
-struct DNNLType<c10::Half> {
-  static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f16;
-};
-
-template <typename T>
-constexpr inline dnnl::memory::data_type get_dnnl_type() {
-  return DNNLType<std::decay_t<T>>::type;
-}
-};  // namespace
-
-template <bool InputNoScale>
-class DNNLPrimitiveHelper {
- public:
-  // I8 input GEMM kernel (C = a_scales * A @ (b_scales * B^T) + bias)
-  // A: [M, K], row-major
-  // B: [K, N], column-major
-  // C: [M, N], row-major
-  // bias: [N], row-major, optional
-  // a_scales: [MS]
-  // b_scales: [NS]
-  // Note: Due to the limitation of oneDNN
-  // (https://github.com/oneapi-src/oneDNN/issues/1636), the quantized bias is
-  // not supported.
-
-  template <typename OutputT, typename BiasT>
-  static void gemm_s8s8_jit(const int8_t* a, const int8_t* b, OutputT* c,
-                            const BiasT* bias, dnnl_dim_t M, dnnl_dim_t N,
-                            dnnl_dim_t K, const float* a_scales,
-                            const float* b_scales, dnnl_dim_t MS,
-                            dnnl_dim_t NS) {
-    auto&& OutputType = get_dnnl_type<OutputT>();
-    auto&& BiasType = get_dnnl_type<BiasT>();
-
-    dnnl::memory::desc a_md({M, K}, dnnl::memory::data_type::s8, {K, 1});
-    dnnl::memory::desc b_md({K, N}, dnnl::memory::data_type::s8, {1, K});
-    dnnl::memory::desc c_md({M, N}, OutputType, {N, 1});
-
-    dnnl::primitive_attr attr;
-    if constexpr (!InputNoScale) {
-      if (MS == 1) {
-        // per-tensor
-        attr.set_scales_mask(DNNL_ARG_SRC, 0);
-      } else {
-        // per-token
-        TORCH_CHECK(false, "per-token quantization is unsupported.");
-      }
-    }
-
-    if (NS == 1) {
-      // per-tensor
-      attr.set_scales_mask(DNNL_ARG_WEIGHTS, 0);
-    } else {
-      // per-channel
-      attr.set_scales_mask(DNNL_ARG_WEIGHTS, 2);
-    }
-
-    dnnl::matmul::primitive_desc matmul_pd;
-// Create memory descriptors with format_tag::any for the primitive. This
-// enables the matmul primitive to choose memory layouts for an
-// optimized primitive implementation, and these layouts may differ from the
-// ones provided by the user.
-#ifdef __aarch64__
-    auto mat_src_md = dnnl::memory::desc({M, K}, dnnl::memory::data_type::s8,
-                                         dnnl::memory::format_tag::any);
-    auto mat_weights_md = dnnl::memory::desc(
-        {K, N}, dnnl::memory::data_type::s8, dnnl::memory::format_tag::any);
-    auto mat_dst_md =
-        dnnl::memory::desc({M, N}, OutputType, dnnl::memory::format_tag::any);
-    if (bias) {
-      dnnl::memory::desc bias_md({1, N}, BiasType, {N, 1});
-      matmul_pd = dnnl::matmul::primitive_desc(default_engine(), mat_src_md,
-                                               mat_weights_md, bias_md,
-                                               mat_dst_md, attr);
-    } else {
-      matmul_pd = dnnl::matmul::primitive_desc(
-          default_engine(), mat_src_md, mat_weights_md, mat_dst_md, attr);
-    }
-#else
-    if (bias) {
-      dnnl::memory::desc bias_md({1, N}, BiasType, {N, 1});
-      matmul_pd = dnnl::matmul::primitive_desc(default_engine(), a_md, b_md,
-                                               bias_md, c_md, attr);
-    } else {
-      matmul_pd = dnnl::matmul::primitive_desc(default_engine(), a_md, b_md,
-                                               c_md, attr);
-    }
-#endif
-    dnnl::matmul matmul(matmul_pd);
-
-    auto& engine = default_engine();
-
-    dnnl::memory a_m(a_md, engine, (void*)a);
-    dnnl::memory b_m(b_md, engine, (void*)b);
-    dnnl::memory c_m(c_md, engine, (void*)c);
-    dnnl::memory a_scales_m({{MS}, dnnl::memory::data_type::f32, {1}}, engine,
-                            (void*)a_scales);
-    dnnl::memory b_scales_m({{NS}, dnnl::memory::data_type::f32, {1}}, engine,
-                            (void*)b_scales);
-
-    auto& stream = default_stream();
-
-    auto mat_src_mem = a_m;
-    auto mat_weights_mem = b_m;
-    auto mat_dst_mem = c_m;
-#ifdef __aarch64__
-    if (matmul_pd.weights_desc() != b_m.get_desc()) {
-      mat_weights_mem = dnnl::memory(matmul_pd.weights_desc(), engine);
-      dnnl::reorder(b_m, mat_weights_mem).execute(stream, b_m, mat_weights_mem);
-    }
-#endif
-    if constexpr (InputNoScale) {
-      if (bias) {
-        dnnl::memory::desc bias_md({N}, BiasType, {1});
-        dnnl::memory bias_m(bias_md, engine, (void*)bias);
-        matmul.execute(
-            stream, {
-                        {DNNL_ARG_SRC, mat_src_mem},
-                        {DNNL_ARG_WEIGHTS, mat_weights_mem},
-                        {DNNL_ARG_BIAS, bias_m},
-                        {DNNL_ARG_DST, mat_dst_mem},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
-                    });
-      } else {
-        matmul.execute(
-            stream, {
-                        {DNNL_ARG_SRC, mat_src_mem},
-                        {DNNL_ARG_WEIGHTS, mat_weights_mem},
-                        {DNNL_ARG_DST, mat_dst_mem},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
-                    });
-      }
-    } else {
-      if (bias) {
-        dnnl::memory::desc bias_md({N}, BiasType, {1});
-        dnnl::memory bias_m(bias_md, engine, (void*)bias);
-        matmul.execute(
-            stream, {
-                        {DNNL_ARG_SRC, mat_src_mem},
-                        {DNNL_ARG_WEIGHTS, mat_weights_mem},
-                        {DNNL_ARG_BIAS, bias_m},
-                        {DNNL_ARG_DST, mat_dst_mem},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, a_scales_m},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
-                    });
-      } else {
-        matmul.execute(
-            stream, {
-                        {DNNL_ARG_SRC, mat_src_mem},
-                        {DNNL_ARG_WEIGHTS, mat_weights_mem},
-                        {DNNL_ARG_DST, mat_dst_mem},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, a_scales_m},
-                        {DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
-                    });
-      }
-    }
-    stream.wait();
-  }
-
- private:
-  static dnnl::engine& default_engine() {
-    static dnnl::engine engine(dnnl::engine::kind::cpu, 0);
-    return engine;
-  }
-
-  static dnnl::stream& default_stream() {
-    static dnnl::stream stream(default_engine());
-    return stream;
-  }
-};
-#endif
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