sidebar-title	Custom Dataset Guide

Custom Dataset Guide

Benchmark LLMs with your own data using single-turn requests, multi-turn conversations, or random sampling.

Overview

AIPerf supports three custom dataset types for benchmarking with your own data:

Dataset Type	Best For	Multi-Turn	Timing Control	Random Sampling
Single Turn	Independent single requests	No	Yes	No
Multi Turn	Conversations with context	Yes	Yes (per turn)	No
Random Pool	Load testing with variety	No	No	Yes

All three support:

Client-side batching
Automatic media handling: local files are converted to base64 format, while remote URLs are sent directly to the API

Server Setup

Start a vLLM server for testing:

docker pull vllm/vllm-openai:latest
docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
  --model Qwen/Qwen3-0.6B \
  --host 0.0.0.0 --port 8000 &

Verify the server is ready:

curl -s http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "test"}],
    "max_tokens": 10
  }' | jq

Single-Turn Datasets

Each line represents one independent single-turn request.

When to Use

Use single_turn when you need deterministic, sequential execution where requests always run in the exact order they appear in the file:

Debugging: Test specific prompts in a known sequence
Regression testing: Same input file → same output order every time
Timing control: Schedule requests with precise timestamps or delays
Predictable testing: Know exactly which request runs when

Execution: Sequential by default (request 1, then 2, then 3, etc.) Input: Single JSONL file only

Basic Text Example

cat > prompts.jsonl << 'EOF'
{"text": "What is machine learning?"}
{"text": "Explain neural networks."}
{"text": "How does backpropagation work?"}
{"text": "What are transformers?"}
{"text": "Define reinforcement learning."}
EOF

aiperf profile \
    --model Qwen/Qwen3-0.6B \
    --endpoint-type chat \
    --input-file prompts.jsonl \
    --custom-dataset-type single_turn \
    --streaming \
    --url localhost:8000 \
    --concurrency 2 \
    --request-count 10

Output:



                                     NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃               Metric ┃      avg ┃      min ┃      max ┃      p99 ┃      p90 ┃      p50 ┃      std ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│  Time to First Token │    19.99 │    12.53 │    49.62 │    48.89 │    42.24 │    13.93 │    12.92 │
│                 (ms) │          │          │          │          │          │          │          │
│ Time to Second Token │     3.81 │     2.01 │     8.25 │     7.94 │     5.15 │     3.36 │     1.62 │
│                 (ms) │          │          │          │          │          │          │          │
│ Time to First Output │    19.99 │    12.53 │    49.62 │    48.89 │    42.24 │    13.93 │    12.92 │
│           Token (ms) │          │          │          │          │          │          │          │
│ Request Latency (ms) │ 2,940.39 │ 1,536.67 │ 7,319.35 │ 7,034.86 │ 4,474.42 │ 2,239.67 │ 1,611.04 │
│  Inter Token Latency │     3.52 │     3.47 │     3.64 │     3.63 │     3.56 │     3.50 │     0.05 │
│                 (ms) │          │          │          │          │          │          │          │
│         Output Token │   284.54 │   274.60 │   288.35 │   288.33 │   288.13 │   285.38 │     3.98 │
│  Throughput Per User │          │          │          │          │          │          │          │
│    (tokens/sec/user) │          │          │          │          │          │          │          │
│      Output Sequence │   833.40 │   438.00 │ 2,106.00 │ 2,022.21 │ 1,268.10 │   626.50 │   465.81 │
│      Length (tokens) │          │          │          │          │          │          │          │
│       Input Sequence │     5.00 │     4.00 │     7.00 │     7.00 │     7.00 │     5.00 │     1.10 │
│      Length (tokens) │          │          │          │          │          │          │          │
│         Output Token │   527.06 │      N/A │      N/A │      N/A │      N/A │      N/A │      N/A │
│           Throughput │          │          │          │          │          │          │          │
│         (tokens/sec) │          │          │          │          │          │          │          │
│   Request Throughput │     0.63 │      N/A │      N/A │      N/A │      N/A │      N/A │      N/A │
│       (requests/sec) │          │          │          │          │          │          │          │
│        Request Count │    10.00 │      N/A │      N/A │      N/A │      N/A │      N/A │      N/A │
│           (requests) │          │          │          │          │          │          │          │
└──────────────────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┘

CLI Command: aiperf profile --model 'Qwen/Qwen3-0.6B' --endpoint-type 'chat' --input-file
'prompts.jsonl' --custom-dataset-type 'single_turn' --streaming --url 'localhost:8000' --concurrency
2
Benchmark Duration: 15.81 sec
CSV Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/profile_export_aiperf.csv
JSON Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/profile_export_aiperf.json
Log File: artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/logs/aiperf.log

Inline alternative

Same content as prompts.jsonl, embedded in the AIPerf YAML config:

benchmark:
  model: Qwen/Qwen3-0.6B
  endpoint:
    url: http://localhost:8000
    type: chat
  dataset:
    type: file
    format: single_turn
    records:
      - {text: "What is machine learning?"}
      - {text: "Explain neural networks."}
      - {text: "How does backpropagation work?"}
      - {text: "What are transformers?"}
      - {text: "Define reinforcement learning."}
  phases:
    type: concurrency
    concurrency: 2
    requests: 100

See Inline Datasets for the full feature reference.

Per-Request Output Length

Control the maximum output tokens per request using the output_length field:

cat > prompts_with_osl.jsonl << 'EOF'
{"text": "Write a haiku about mountains.", "output_length": 50}
{"text": "Explain quantum computing in detail.", "output_length": 500}
{"text": "What is 2+2?", "output_length": 10}
{"text": "Summarize machine learning."}
EOF

aiperf profile \
    --model Qwen/Qwen3-0.6B \
    --endpoint-type chat \
    --input-file prompts_with_osl.jsonl \
    --custom-dataset-type single_turn \
    --streaming \
    --url localhost:8000 \
    --osl 200 \
    --request-count 10

Precedence: Per-line output_length takes priority over the global --osl flag. Lines without output_length fall back to --osl if set (200 in this example), or let the server decide the output length.

The output_length field also works per-turn in multi_turn datasets.

Per-Request `extra`

Send vendor-specific or sampling parameters per request via the extra field. The dict is shallow-merged into the top of the request body at dispatch. Per-line keys win over --extra-inputs:

cat > prompts_with_extra.jsonl << 'EOF'
{"text": "Brainstorm a haiku.", "extra": {"temperature": 1.2, "top_p": 0.9}}
{"text": "Explain quantum computing.", "extra": {"temperature": 0.2, "seed": 42}}
{"text": "Summarize ML.", "extra": {"min_tokens": 50, "ignore_eos": true}}
EOF

The extra field also works per-turn in multi_turn datasets.

Multi-Turn Datasets

Each entry represents a complete conversation with multiple turns.

When to Use

Use multi_turn when you need conversations with context where each turn builds on previous turns in the conversation:

Chat testing: Test conversational AI that maintains context across turns
Realistic interactions: Simulate real user conversations with follow-up questions
Task completion: Test multi-step tasks that require conversation history

Execution: Sequential within each conversation (turn 1, then 2, then 3, etc.), but multiple conversations run concurrently Input: Single JSONL file only

Basic Conversation

cat > conversations.jsonl << 'EOF'
{"session_id": "chat_1", "turns": [{"text": "What is machine learning?"}, {"text": "Can you give me an example?"}]}
{"session_id": "chat_2", "turns": [{"text": "Explain neural networks."}, {"text": "How do they differ from traditional algorithms?"}, {"text": "Which architecture for image classification?"}]}
EOF

aiperf profile \
    --model Qwen/Qwen3-0.6B \
    --endpoint-type chat \
    --input-file conversations.jsonl \
    --custom-dataset-type multi_turn \
    --streaming \
    --url localhost:8000 \
    --concurrency 2 \
    --request-count 10

Output:

                                     NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┓
┃                 Metric ┃      avg ┃      min ┃      max ┃      p99 ┃      p90 ┃      p50 ┃    std ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━┩
│    Time to First Token │    23.17 │    11.83 │    56.70 │    55.34 │    43.06 │    18.00 │  13.66 │
│                   (ms) │          │          │          │          │          │          │        │
│   Time to Second Token │     4.77 │     2.29 │    15.41 │    14.65 │     7.73 │     3.44 │   3.74 │
│                   (ms) │          │          │          │          │          │          │        │
│   Time to First Output │    23.17 │    11.83 │    56.70 │    55.34 │    43.06 │    18.00 │  13.66 │
│             Token (ms) │          │          │          │          │          │          │        │
│   Request Latency (ms) │ 2,008.84 │ 1,348.13 │ 3,045.04 │ 3,007.53 │ 2,669.92 │ 2,082.32 │ 572.34 │
│    Inter Token Latency │     3.50 │     3.13 │     3.67 │     3.67 │     3.62 │     3.52 │   0.14 │
│                   (ms) │          │          │          │          │          │          │        │
│           Output Token │   286.03 │   272.35 │   319.58 │   316.89 │   292.60 │   283.77 │  12.33 │
│    Throughput Per User │          │          │          │          │          │          │        │
│      (tokens/sec/user) │          │          │          │          │          │          │        │
│ Output Sequence Length │   565.60 │   380.00 │   838.00 │   826.57 │   723.70 │   581.50 │ 150.96 │
│               (tokens) │          │          │          │          │          │          │        │
│  Input Sequence Length │   379.80 │     5.00 │ 1,331.00 │ 1,287.80 │   899.00 │   203.00 │ 438.88 │
│               (tokens) │          │          │          │          │          │          │        │
│           Output Token │   533.83 │      N/A │      N/A │      N/A │      N/A │      N/A │    N/A │
│             Throughput │          │          │          │          │          │          │        │
│           (tokens/sec) │          │          │          │          │          │          │        │
│     Request Throughput │     0.94 │      N/A │      N/A │      N/A │      N/A │      N/A │    N/A │
│         (requests/sec) │          │          │          │          │          │          │        │
│          Request Count │    10.00 │      N/A │      N/A │      N/A │      N/A │      N/A │    N/A │
│             (requests) │          │          │          │          │          │          │        │
└────────────────────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴────────┘

CLI Command: aiperf profile --model 'Qwen/Qwen3-0.6B' --endpoint-type 'chat' --input-file
'conversations.jsonl' --custom-dataset-type 'multi_turn' --streaming --url 'localhost:8000'
--concurrency 2
Benchmark Duration: 10.60 sec
CSV Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/profile_export_aiperf.csv
JSON Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/profile_export_aiperf.json
Log File: artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency2/logs/aiperf.log

Key Points:

Each turn includes full conversation history
Turns execute sequentially within each conversation
Multiple conversations run concurrently (up to --concurrency)
Each turn supports output_length and extra (same semantics as single_turn — vendor extras shallow-merged into the top of the wire body, latest turn wins for chat-style endpoints)

Inline alternative

benchmark:
  model: Qwen/Qwen3-0.6B
  endpoint:
    url: http://localhost:8000
    type: chat
  dataset:
    type: file
    format: multi_turn
    records:
      - session_id: chat_1
        turns:
          - {text: "What is machine learning?"}
          - {text: "Can you give me an example?"}
      - session_id: chat_2
        turns:
          - {text: "Explain neural networks."}
          - {text: "How do they differ from traditional algorithms?"}
          - {text: "Which architecture for image classification?"}
  phases:
    type: concurrency
    concurrency: 2
    requests: 100

Random Pool Datasets

Randomly sample from one or more data pools for varied request patterns.

When to Use

Use random_pool when you need random sampling with replacement for unpredictable, varied request patterns:

Load testing: Generate diverse request patterns with variety
Production simulation: Model real-world workloads where requests vary
Stress testing: Test system behavior under mixed input patterns
Multiple data sources: Combine files from a directory (each file becomes a pool)

Execution: Random sampling with replacement (same entry can be selected multiple times) Input: Single JSONL file OR directory of multiple JSONL files Note: Does NOT support timing control or multi-turn conversations

Basic Single-File Sampling

cat > pool.jsonl << 'EOF'
{"text": "What is machine learning?"}
{"text": "Explain neural networks."}
{"text": "How does backpropagation work?"}
{"text": "What are transformers?"}
{"text": "Define reinforcement learning."}
{"text": "What is transfer learning?"}
{"text": "Explain gradient descent."}
{"text": "What are GANs?"}
EOF

aiperf profile \
    --model Qwen/Qwen3-0.6B \
    --endpoint-type chat \
    --input-file pool.jsonl \
    --custom-dataset-type random_pool \
    --num-conversations 50 \
    --streaming \
    --concurrency 4 \
    --random-seed 42 \
    --url localhost:8000

Output:

                                     NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃              Metric ┃      avg ┃      min ┃       max ┃      p99 ┃      p90 ┃      p50 ┃      std ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ Time to First Token │    17.73 │    12.25 │     53.21 │    53.17 │    19.85 │    14.63 │     9.90 │
│                (ms) │          │          │           │          │          │          │          │
│      Time to Second │     3.73 │     2.20 │     10.38 │     7.68 │     4.08 │     3.66 │     1.10 │
│          Token (ms) │          │          │           │          │          │          │          │
│       Time to First │    17.73 │    12.25 │     53.21 │    53.17 │    19.85 │    14.63 │     9.90 │
│   Output Token (ms) │          │          │           │          │          │          │          │
│     Request Latency │ 3,321.54 │ 1,356.57 │ 10,393.82 │ 9,063.81 │ 5,372.92 │ 2,917.73 │ 1,644.46 │
│                (ms) │          │          │           │          │          │          │          │
│ Inter Token Latency │     3.81 │     3.53 │      4.17 │     4.15 │     3.97 │     3.79 │     0.12 │
│                (ms) │          │          │           │          │          │          │          │
│        Output Token │   262.66 │   239.55 │    283.24 │   279.36 │   270.36 │   264.13 │     8.25 │
│ Throughput Per User │          │          │           │          │          │          │          │
│   (tokens/sec/user) │          │          │           │          │          │          │          │
│     Output Sequence │   861.02 │   369.00 │  2,615.00 │ 2,255.83 │ 1,306.40 │   766.00 │   404.28 │
│     Length (tokens) │          │          │           │          │          │          │          │
│      Input Sequence │     5.00 │     4.00 │      7.00 │     7.00 │     6.10 │     5.00 │     0.96 │
│     Length (tokens) │          │          │           │          │          │          │          │
│        Output Token │ 1,007.36 │      N/A │       N/A │      N/A │      N/A │      N/A │      N/A │
│          Throughput │          │          │           │          │          │          │          │
│        (tokens/sec) │          │          │           │          │          │          │          │
│  Request Throughput │     1.17 │      N/A │       N/A │      N/A │      N/A │      N/A │      N/A │
│      (requests/sec) │          │          │           │          │          │          │          │
│       Request Count │    50.00 │      N/A │       N/A │      N/A │      N/A │      N/A │      N/A │
│          (requests) │          │          │           │          │          │          │          │
└─────────────────────┴──────────┴──────────┴───────────┴──────────┴──────────┴──────────┴──────────┘

CLI Command: aiperf profile --model 'Qwen/Qwen3-0.6B' --endpoint-type 'chat' --input-file
'pool.jsonl' --custom-dataset-type 'random_pool' --num-conversations 50 --streaming --concurrency 4
--random-seed 42 --url 'localhost:8000'
Benchmark Duration: 42.74 sec
CSV Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency4/profile_export_aiperf.csv
JSON Export:
artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency4/profile_export_aiperf.json
Log File: artifacts/Qwen_Qwen3-0.6B-openai-chat-concurrency4/logs/aiperf.log

Behavior:

Randomly samples 50 requests from 8-entry pool
Sampling with replacement (entries can repeat)
Use --random-seed for reproducibility

Inline alternative (multi-pool)

benchmark:
  model: Qwen/Qwen3-0.6B
  endpoint:
    url: http://localhost:8000
    type: chat
  dataset:
    type: file
    format: random_pool
    sampling: random
    records:
      queries:
        - {text: "What is your refund policy?", type: random_pool}
        - {text: "How do I reset my password?", type: random_pool}
      passages:
        - {text: "Refunds are processed within 5 business days.", type: random_pool}
        - {text: "Click 'Forgot password' on the login page.", type: random_pool}
  phases:
    type: concurrency
    concurrency: 2
    requests: 50

Multi-Turn Conversations - Multi-turn conversation benchmarking
Conversation Context Mode - How conversation history accumulates in multi-turn

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Custom Dataset Guide

Overview

Server Setup

Single-Turn Datasets

When to Use

Basic Text Example

Inline alternative

Per-Request Output Length

Per-Request `extra`

Multi-Turn Datasets

When to Use

Basic Conversation

Inline alternative

Random Pool Datasets

When to Use

Basic Single-File Sampling

Inline alternative (multi-pool)

Related

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Custom Dataset Guide

Overview

Server Setup

Single-Turn Datasets

When to Use

Basic Text Example

Inline alternative

Per-Request Output Length

Per-Request extra

Multi-Turn Datasets

When to Use

Basic Conversation

Inline alternative

Random Pool Datasets

When to Use

Basic Single-File Sampling

Inline alternative (multi-pool)

Related

Per-Request `extra`