| sidebar-title | Profile with ShareGPT Dataset |
|---|
AIPerf supports benchmarking using the ShareGPT dataset, which contains real conversational data from user interactions.
This guide covers profiling OpenAI-compatible chat completions endpoints using the ShareGPT public dataset.
Launch a vLLM server with a chat model:
docker pull vllm/vllm-openai:latest
docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
--model Qwen/Qwen3-0.6BVerify the server is ready:
curl -s localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"Qwen/Qwen3-0.6B","messages":[{"role":"user","content":"test"}],"max_tokens":1}'AIPerf automatically downloads and caches the ShareGPT dataset from HuggingFace.
aiperf profile \
--model Qwen/Qwen3-0.6B \
--endpoint-type chat \
--streaming \
--url localhost:8000 \
--public-dataset sharegpt \
--request-count 20 \
--concurrency 4Sample Output (Successful Run):
INFO Starting AIPerf System
INFO Downloading ShareGPT dataset from HuggingFace
INFO Cached ShareGPT dataset loaded
INFO AIPerf System is PROFILING
Profiling: 20/20 |████████████████████████| 100% [00:45<00:00]
INFO Benchmark completed successfully
INFO Results saved to: artifacts/Qwen_Qwen3-0.6B-chat-concurrency4/
NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ Metric ┃ avg ┃ min ┃ max ┃ p99 ┃ p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ Request Latency (ms) │ 1456.78 │ 1089.34 │ 1978.90 │ 1898.45 │ 1423.67 │
│ Time to First Token (ms) │ 267.89 │ 198.34 │ 389.12 │ 367.45 │ 262.12 │
│ Inter Token Latency (ms) │ 13.45 │ 10.67 │ 18.90 │ 17.89 │ 13.12 │
│ Output Token Count (tokens) │ 187.00 │ 142.00 │ 245.00 │ 239.00 │ 184.00 │
│ Request Throughput (req/s) │ 8.45 │ - │ - │ - │ - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘
JSON Export: artifacts/Qwen_Qwen3-0.6B-chat-concurrency4/profile_export_aiperf.json