[V1] Enable multi-input by default (#15799)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/models/supported_models.md

@@ -759,7 +759,7 @@ On the other hand, modalities separated by `/` are mutually exclusive.
 See [this page](#multimodal-inputs) on how to pass multi-modal inputs to the model.
 
 :::{important}
-To enable multiple multi-modal items per text prompt, you have to set `limit_mm_per_prompt` (offline inference)
+**To enable multiple multi-modal items per text prompt in vLLM V0**, you have to set `limit_mm_per_prompt` (offline inference)
 or `--limit-mm-per-prompt` (online serving). For example, to enable passing up to 4 images per text prompt:
 
 Offline inference:
@@ -777,6 +777,8 @@ Online serving:
 vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt image=4
 ```
 
+**This is no longer required if you are using vLLM V1.**
+
 :::
 
 :::{note}

docs/source/serving/offline_inference.md

@@ -110,6 +110,30 @@ If you run out of CPU RAM, try the following options:
 - (Multi-modal models only) you can set the size of multi-modal input cache using `VLLM_MM_INPUT_CACHE_GIB` environment variable (default 4 GiB).
 - (CPU backend only) you can set the size of KV cache using `VLLM_CPU_KVCACHE_SPACE` environment variable (default 4 GiB).
 
+#### Disable unused modalities
+
+You can disable unused modalities (except for text) by setting its limit to zero.
+
+For example, if your application only accepts image input, there is no need to allocate any memory for videos.
+
+```python
+from vllm import LLM
+
+# Accept images but not videos
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          limit_mm_per_prompt={"video": 0})
+```
+
+You can even run a multi-modal model for text-only inference:
+
+```python
+from vllm import LLM
+
+# Don't accept images. Just text.
+llm = LLM(model="google/gemma-3-27b-it",
+          limit_mm_per_prompt={"image": 0})
+```
+
 ### Performance optimization and tuning
 
 You can potentially improve the performance of vLLM by finetuning various options.