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enginex-ascend-910-vllm/benchmarks/scripts/perf_result_template.md
2025-09-09 09:40:35 +08:00

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Online serving tests

  • Input length: randomly sample 200 prompts from ShareGPT and lmarena-ai/vision-arena-bench-v0.1(multi-modal) dataset (with fixed random seed).
  • Output length: the corresponding output length of these 200 prompts.
  • Batch size: dynamically determined by vllm and the arrival pattern of the requests.
  • Average QPS (query per second): 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
  • Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
  • Evaluation metrics: throughput, TTFT (median time to the first token ), ITL (median inter-token latency) TPOT(median time per output token).

{serving_tests_markdown_table}

Offline tests

Latency tests

  • Input length: 32 tokens.
  • Output length: 128 tokens.
  • Batch size: fixed (8).
  • Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
  • Evaluation metrics: end-to-end latency.

{latency_tests_markdown_table}

Throughput tests

  • Input length: randomly sample 200 prompts from ShareGPT and lmarena-ai/vision-arena-bench-v0.1(multi-modal) dataset (with fixed random seed).
  • Output length: the corresponding output length of these 200 prompts.
  • Batch size: dynamically determined by vllm to achieve maximum throughput.
  • Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
  • Evaluation metrics: throughput.

{throughput_tests_markdown_table}