Profile with Bailian Traces

AIPerf supports benchmarking using the Bailian usage traces, a public dataset of anonymized production chat traces from Qwen model serving. The dataset contains both single-turn requests and multi-turn conversations.

This guide covers replaying Bailian traces with precise timing to reproduce real-world traffic patterns.

Start a vLLM Server

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/Qwen3-0.6B

Verify 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}'

Bailian Trace Format

Bailian traces are JSONL files where each line represents a single request.

• chat_id: Randomized unique chat identifier
• timestamp: Request arrival time in seconds (converted to milliseconds internally)
• input_length: Input token count
• output_length: Output token count
• parent_chat_id: Parent chat ID linking turns in a session; -1 for root (default: -1)
• type: Request type (text, search, image, file)
• turn: Conversation turn number (default: 1)
• hash_ids: Salted SipHash block IDs for KV cache simulation (16 tokens per block)

Example entries:

1
{"chat_id": 159, "parent_chat_id": -1, "timestamp": 61.114, "input_length": 521, "output_length": 132, "type": "text", "turn": 1}
2
{"chat_id": 160, "parent_chat_id": 159, "timestamp": 62.5, "input_length": 400, "output_length": 80, "type": "text", "turn": 2}

Entries with the same root chat_id form a session and are replayed in turn order.

Download and Profile

Download a trace file from the public Bailian dataset:

The repository includes four traces representing different workload types: qwen_traceA_blksz_16.jsonl, qwen_traceB_blksz_16.jsonl, qwen_coder_blksz_16.jsonl, and qwen_thinking_blksz_16.jsonl. Substitute any of them in the command below.

$
# Download a trace file
$
curl -Lo qwen_traceA_blksz_16.jsonl \
>
  https://github.com/alibaba-edu/qwen-bailian-usagetraces-anon/raw/refs/heads/main/qwen_traceA_blksz_16.jsonl
$
$
# Create a small subset for a quick test
$
head -n 50 qwen_traceA_blksz_16.jsonl > bailian_short.jsonl
$
$
# Run trace replay
$
aiperf profile \
>
    --model Qwen/Qwen3-0.6B \
>
    --endpoint-type chat \
>
    --streaming \
>
    --url localhost:8000 \
>
    --input-file bailian_short.jsonl \
>
    --custom-dataset-type bailian_trace \
>
    --fixed-schedule

Related Tutorials

• Trace Benchmarking with Mooncake - Mooncake FAST’25 trace replay
• Fixed Schedule - Precise timestamp-based execution for any dataset
• Prefix Synthesis - KV cache testing with hash-based prefix data
• Multi-Turn Conversations - Multi-turn conversation benchmarking
• Conversation Context Mode - How conversation history accumulates in multi-turn