[Doc] Update docs on handling OOM (#15357)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Roger Wang <ywang@roblox.com>
Co-authored-by: Roger Wang <ywang@roblox.com>

docs/source/serving/engine_args.md

@@ -2,7 +2,12 @@
 
 # Engine Arguments
 
-Below, you can find an explanation of every engine argument for vLLM:
+Engine arguments control the behavior of the vLLM engine.
+
+- For [offline inference](#offline-inference), they are part of the arguments to `LLM` class.
+- For [online serving](#openai-compatible-server), they are part of the arguments to `vllm serve`.
+
+Below, you can find an explanation of every engine argument:
 
 <!--- pyml disable-num-lines 7 no-space-in-emphasis -->
 ```{eval-rst}
@@ -15,7 +20,7 @@ Below, you can find an explanation of every engine argument for vLLM:
 
 ## Async Engine Arguments
 
-Below are the additional arguments related to the asynchronous engine:
+Additional arguments are available to the asynchronous engine which is used for online serving:
 
 <!--- pyml disable-num-lines 7 no-space-in-emphasis -->
 ```{eval-rst}

docs/source/serving/offline_inference.md

@@ -97,6 +97,13 @@ llm = LLM(model="adept/fuyu-8b",
           max_num_seqs=2)
 ```
 
+#### Adjust cache size
+
+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).
+
 ### Performance optimization and tuning
 
 You can potentially improve the performance of vLLM by finetuning various options.