Throughput Optimization Solution
To improve the throughput of the vLLM model service, you can do the following:
- Use the preset program:Enable high-throughput optimization by directly specifying the -profile high_throughput parameter
- Adjust the parallel parameters:Increase tensor parallelism with -tensor-parallel-size (requires multi-GPU support)
- Quantitative optimization:Add quantization parameters such as -quantization awq to reduce video memory usage
- Batch optimization:Adjusting the -max-num-batched-tokens and -max-num-seqs parameters
Note: Throughput increase may increase latency, need to be balanced according to the actual application scenario. It is recommended to monitor GPU utilization with vllm-cli status first, and consider enabling FP8 quantization (-quantization fp8) if a video memory bottleneck is found. For MoE architecture models, the moe_optimized configuration should be used instead.
This answer comes from the articlevLLM CLI: Command Line Tool for Deploying Large Language Models with vLLMThe
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