@@ -168,17 +168,18 @@ Note, it is recommended to manually reserve 1 CPU for vLLM front-end process whe
### How to do performance tuning for vLLM CPU?
-First of all, please make sure the thread-binding and KV cache space are properly set and take effect. You can check the thread-binding by running a vLLM benchmark and observing CPU cores usage via `htop`.
First of all, please make sure the thread-binding and KV cache space are properly set and take effect. You can check the thread-binding by running a vLLM benchmark and observing CPU cores usage via `htop`.
- Inference batch size is a important parameter for the performance. Larger batch usually provides higher throughput, smaller batch provides lower latency. Tuning max batch size starts from default value to balance throughput and latency is an effective way to improve vLLM CPU performance on specific platforms. There are two important related parameters in vLLM:
-`--max-num-batched-tokens`, defines the limit of token numbers in a single batch, has more impacts on the first token performance. The default value is set as:
Inference batch size is a important parameter for the performance. Larger batch usually provides higher throughput, smaller batch provides lower latency. Tuning max batch size starts from default value to balance throughput and latency is an effective way to improve vLLM CPU performance on specific platforms. There are two important related parameters in vLLM:
-`--max-num-batched-tokens`, defines the limit of token numbers in a single batch, has more impacts on the first token performance. The default value is set as:
- Offline Inference: `4096 * world_size`
- Online Serving: `2048 * world_size`
-`--max-num-seqs`, defines the limit of sequence numbers in a single batch, has more impacts on the output token performance.
-`--max-num-seqs`, defines the limit of sequence numbers in a single batch, has more impacts on the output token performance.
- Offline Inference: `256 * world_size`
- Online Serving: `128 * world_size`
-vLLM CPU supports tensor parallel (TP) and pipeline parallel (PP) to leverage multiple CPU sockets and memory nodes. For more detials of tuning TP and PP, please refer to [Optimization and Tuning](../../configuration/optimization.md). For vLLM CPU, it is recommend to use TP and PP togther if there are enough CPU sockets and memory nodes.
vLLM CPU supports tensor parallel (TP) and pipeline parallel (PP) to leverage multiple CPU sockets and memory nodes. For more detials of tuning TP and PP, please refer to [Optimization and Tuning](../../configuration/optimization.md). For vLLM CPU, it is recommend to use TP and PP togther if there are enough CPU sockets and memory nodes.
### Which quantization configs does vLLM CPU support?
jiang1.li <jiang1.li@intel.com>