Update default max_num_batch_tokens for chunked prefill (#11694)

docs/source/usage/performance.md

@@ -32,8 +32,8 @@ You can enable the feature by specifying `--enable-chunked-prefill` in the comma
 ```python
 llm = LLM(model="meta-llama/Llama-2-7b-hf", enable_chunked_prefill=True)
 # Set max_num_batched_tokens to tune performance.
-# NOTE: 512 is the default max_num_batched_tokens for chunked prefill.
-# llm = LLM(model="meta-llama/Llama-2-7b-hf", enable_chunked_prefill=True, max_num_batched_tokens=512)
+# NOTE: 2048 is the default max_num_batched_tokens for chunked prefill.
+# llm = LLM(model="meta-llama/Llama-2-7b-hf", enable_chunked_prefill=True, max_num_batched_tokens=2048)
 ```
 
 By default, vLLM scheduler prioritizes prefills and doesn't batch prefill and decode to the same batch.
@@ -49,13 +49,12 @@ This policy has two benefits:
 - It improves ITL and generation decode because decode requests are prioritized.
 - It helps achieve better GPU utilization by locating compute-bound (prefill) and memory-bound (decode) requests to the same batch.
 
-You can tune the performance by changing `max_num_batched_tokens`.
-By default, it is set to 512, which has the best ITL on A100 in the initial benchmark (llama 70B and mixtral 8x22B).
+You can tune the performance by changing `max_num_batched_tokens`. By default, it is set to 2048.
 Smaller `max_num_batched_tokens` achieves better ITL because there are fewer prefills interrupting decodes.
 Higher `max_num_batched_tokens` achieves better TTFT as you can put more prefill to the batch.
 
 - If `max_num_batched_tokens` is the same as `max_model_len`, that's almost the equivalent to the default scheduling policy (except that it still prioritizes decodes).
-- Note that the default value (512) of `max_num_batched_tokens` is optimized for ITL, and it may have lower throughput than the default scheduler.
+- Note that the default value (2048) of `max_num_batched_tokens` is optimized for ITL, and it may have lower throughput than the default scheduler.
 
 We recommend you set `max_num_batched_tokens > 2048` for throughput.