README.md

# Benchmarking vLLM

This README guides you through running benchmark tests with the extensive
datasets supported on vLLM. It’s a living document, updated as new features and datasets
become available.

## Dataset Overview

<table style="width:100%; border-collapse: collapse;">
  <thead>
    <tr>
      <th style="width:15%; text-align: left;">Dataset</th>
      <th style="width:10%; text-align: center;">Online</th>
      <th style="width:10%; text-align: center;">Offline</th>
      <th style="width:65%; text-align: left;">Data Path</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><strong>ShareGPT</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
    </tr>
    <tr>
      <td><strong>ShareGPT4V (Image)</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td>
        <code>wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json</code>
        <br>
        <div>Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:</div>
        <code>wget http://images.cocodataset.org/zips/train2017.zip</code>
      </td>
    </tr>
    <tr>
      <td><strong>BurstGPT</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
    </tr>
    <tr>
      <td><strong>Sonnet</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td>Local file: <code>benchmarks/sonnet.txt</code></td>
    </tr>
    <tr>
      <td><strong>Random</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>synthetic</code></td>
    </tr>
    <tr>
      <td><strong>HuggingFace-VisionArena</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>lmarena-ai/VisionArena-Chat</code></td>
    </tr>
    <tr>
      <td><strong>HuggingFace-InstructCoder</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>likaixin/InstructCoder</code></td>
    </tr>
      <tr>
      <td><strong>HuggingFace-AIMO</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>AI-MO/aimo-validation-aime</code> , <code>AI-MO/NuminaMath-1.5</code>, <code>AI-MO/NuminaMath-CoT</code></td>
    </tr>
    <tr>
      <td><strong>HuggingFace-Other</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
    </tr>
    <tr>
      <td><strong>Custom</strong></td>
      <td style="text-align: center;">✅</td>
      <td style="text-align: center;">✅</td>
      <td>Local file: <code>data.jsonl</code></td>
    </tr>
  </tbody>
</table>

✅: supported

🟡: Partial support

🚧: to be supported

**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`

## 🚀 Example - Online Benchmark

<details>
<summary>Show more</summary>

<br/>

First start serving your model

```bash
vllm serve NousResearch/Hermes-3-Llama-3.1-8B
```

Then run the benchmarking script

```bash
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
vllm bench serve \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --endpoint /v1/completions \
  --dataset-name sharegpt \
  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
  --num-prompts 10
```

If successful, you will see the following output

```text
============ Serving Benchmark Result ============
Successful requests:                     10
Benchmark duration (s):                  5.78
Total input tokens:                      1369
Total generated tokens:                  2212
Request throughput (req/s):              1.73
Output token throughput (tok/s):         382.89
Total Token throughput (tok/s):          619.85
---------------Time to First Token----------------
Mean TTFT (ms):                          71.54
Median TTFT (ms):                        73.88
P99 TTFT (ms):                           79.49
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          7.91
Median TPOT (ms):                        7.96
P99 TPOT (ms):                           8.03
---------------Inter-token Latency----------------
Mean ITL (ms):                           7.74
Median ITL (ms):                         7.70
P99 ITL (ms):                            8.39
==================================================
```

### Custom Dataset

If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl

```json
{"prompt": "What is the capital of India?"}
{"prompt": "What is the capital of Iran?"}
{"prompt": "What is the capital of China?"}
```

```bash
# start server
VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct
```

```bash
# run benchmarking script
vllm bench serve --port 9001 --save-result --save-detailed \
  --backend vllm \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --endpoint /v1/completions \
  --dataset-name custom \
  --dataset-path <path-to-your-data-jsonl> \
  --custom-skip-chat-template \
  --num-prompts 80 \
  --max-concurrency 1 \
  --temperature=0.3 \
  --top-p=0.75 \
  --result-dir "./log/"
```

You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`.

### VisionArena Benchmark for Vision Language Models

```bash
# need a model with vision capability here
vllm serve Qwen/Qwen2-VL-7B-Instruct
```

```bash
vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
  --dataset-path lmarena-ai/VisionArena-Chat \
  --hf-split train \
  --num-prompts 1000
```

### InstructCoder Benchmark with Speculative Decoding

``` bash
VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
    --speculative-config $'{"method": "ngram",
    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
    "prompt_lookup_min": 2}'
```

``` bash
vllm bench serve \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset-name hf \
    --dataset-path likaixin/InstructCoder \
    --num-prompts 2048
```

### Other HuggingFaceDataset Examples

```bash
vllm serve Qwen/Qwen2-VL-7B-Instruct
```

`lmms-lab/LLaVA-OneVision-Data`:

```bash
vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
  --dataset-path lmms-lab/LLaVA-OneVision-Data \
  --hf-split train \
  --hf-subset "chart2text(cauldron)" \
  --num-prompts 10
```

`Aeala/ShareGPT_Vicuna_unfiltered`:

```bash
vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
  --hf-split train \
  --num-prompts 10
```

`AI-MO/aimo-validation-aime`:

``` bash
vllm bench serve \
    --model Qwen/QwQ-32B \
    --dataset-name hf \
    --dataset-path AI-MO/aimo-validation-aime \
    --num-prompts 10 \
    --seed 42
```

`philschmid/mt-bench`:

``` bash
vllm bench serve \
    --model Qwen/QwQ-32B \
    --dataset-name hf \
    --dataset-path philschmid/mt-bench \
    --num-prompts 80
```

### Running With Sampling Parameters

When using OpenAI-compatible backends such as `vllm`, optional sampling
parameters can be specified. Example client command:

```bash
vllm bench serve \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --endpoint /v1/completions \
  --dataset-name sharegpt \
  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
  --top-k 10 \
  --top-p 0.9 \
  --temperature 0.5 \
  --num-prompts 10
```

### Running With Ramp-Up Request Rate

The benchmark tool also supports ramping up the request rate over the
duration of the benchmark run. This can be useful for stress testing the
server or finding the maximum throughput that it can handle, given some latency budget.

Two ramp-up strategies are supported:

- `linear`: Increases the request rate linearly from a start value to an end value.
- `exponential`: Increases the request rate exponentially.

The following arguments can be used to control the ramp-up:

- `--ramp-up-strategy`: The ramp-up strategy to use (`linear` or `exponential`).
- `--ramp-up-start-rps`: The request rate at the beginning of the benchmark.
- `--ramp-up-end-rps`: The request rate at the end of the benchmark.

</details>

## 📈 Example - Offline Throughput Benchmark

<details>
<summary>Show more</summary>

<br/>

```bash
vllm bench throughput \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset-name sonnet \
  --dataset-path vllm/benchmarks/sonnet.txt \
  --num-prompts 10
```

If successful, you will see the following output

```text
Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s
Total num prompt tokens:  5014
Total num output tokens:  1500
```

### VisionArena Benchmark for Vision Language Models

```bash
vllm bench throughput \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --backend vllm-chat \
  --dataset-name hf \
  --dataset-path lmarena-ai/VisionArena-Chat \
  --num-prompts 1000 \
  --hf-split train
```

The `num prompt tokens` now includes image token counts

```text
Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s
Total num prompt tokens:  14527
Total num output tokens:  1280
```

### InstructCoder Benchmark with Speculative Decoding

``` bash
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_USE_V1=1 \
vllm bench throughput \
    --dataset-name=hf \
    --dataset-path=likaixin/InstructCoder \
    --model=meta-llama/Meta-Llama-3-8B-Instruct \
    --input-len=1000 \
    --output-len=100 \
    --num-prompts=2048 \
    --async-engine \
    --speculative-config $'{"method": "ngram",
    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
    "prompt_lookup_min": 2}'
```

```text
Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s
Total num prompt tokens:  261136
Total num output tokens:  204800
```

### Other HuggingFaceDataset Examples

`lmms-lab/LLaVA-OneVision-Data`:

```bash
vllm bench throughput \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --backend vllm-chat \
  --dataset-name hf \
  --dataset-path lmms-lab/LLaVA-OneVision-Data \
  --hf-split train \
  --hf-subset "chart2text(cauldron)" \
  --num-prompts 10
```

`Aeala/ShareGPT_Vicuna_unfiltered`:

```bash
vllm bench throughput \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --backend vllm-chat \
  --dataset-name hf \
  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
  --hf-split train \
  --num-prompts 10
```

`AI-MO/aimo-validation-aime`:

```bash
vllm bench throughput \
  --model Qwen/QwQ-32B \
  --backend vllm \
  --dataset-name hf \
  --dataset-path AI-MO/aimo-validation-aime \
  --hf-split train \
  --num-prompts 10
```

Benchmark with LoRA adapters:

``` bash
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
vllm bench throughput \
  --model meta-llama/Llama-2-7b-hf \
  --backend vllm \
  --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
  --dataset_name sharegpt \
  --num-prompts 10 \
  --max-loras 2 \
  --max-lora-rank 8 \
  --enable-lora \
  --lora-path yard1/llama-2-7b-sql-lora-test
  ```

</details>

## 🛠️ Example - Structured Output Benchmark

<details>
<summary>Show more</summary>

<br/>

Benchmark the performance of structured output generation (JSON, grammar, regex).

### Server Setup

```bash
vllm serve NousResearch/Hermes-3-Llama-3.1-8B
```

### JSON Schema Benchmark

```bash
python3 benchmarks/benchmark_serving_structured_output.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset json \
  --structured-output-ratio 1.0 \
  --request-rate 10 \
  --num-prompts 1000
```

### Grammar-based Generation Benchmark

```bash
python3 benchmarks/benchmark_serving_structured_output.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset grammar \
  --structure-type grammar \
  --request-rate 10 \
  --num-prompts 1000
```

### Regex-based Generation Benchmark

```bash
python3 benchmarks/benchmark_serving_structured_output.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset regex \
  --request-rate 10 \
  --num-prompts 1000
```

### Choice-based Generation Benchmark

```bash
python3 benchmarks/benchmark_serving_structured_output.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset choice \
  --request-rate 10 \
  --num-prompts 1000
```

### XGrammar Benchmark Dataset

```bash
python3 benchmarks/benchmark_serving_structured_output.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset xgrammar_bench \
  --request-rate 10 \
  --num-prompts 1000
```

</details>

## 📚 Example - Long Document QA Benchmark

<details>
<summary>Show more</summary>

<br/>

Benchmark the performance of long document question-answering with prefix caching.

### Basic Long Document QA Test

```bash
python3 benchmarks/benchmark_long_document_qa_throughput.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --enable-prefix-caching \
  --num-documents 16 \
  --document-length 2000 \
  --output-len 50 \
  --repeat-count 5
```

### Different Repeat Modes

```bash
# Random mode (default) - shuffle prompts randomly
python3 benchmarks/benchmark_long_document_qa_throughput.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --enable-prefix-caching \
  --num-documents 8 \
  --document-length 3000 \
  --repeat-count 3 \
  --repeat-mode random

# Tile mode - repeat entire prompt list in sequence
python3 benchmarks/benchmark_long_document_qa_throughput.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --enable-prefix-caching \
  --num-documents 8 \
  --document-length 3000 \
  --repeat-count 3 \
  --repeat-mode tile

# Interleave mode - repeat each prompt consecutively
python3 benchmarks/benchmark_long_document_qa_throughput.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --enable-prefix-caching \
  --num-documents 8 \
  --document-length 3000 \
  --repeat-count 3 \
  --repeat-mode interleave
```

</details>

## 🗂️ Example - Prefix Caching Benchmark

<details>
<summary>Show more</summary>

<br/>

Benchmark the efficiency of automatic prefix caching.

### Fixed Prompt with Prefix Caching

```bash
python3 benchmarks/benchmark_prefix_caching.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --enable-prefix-caching \
  --num-prompts 1 \
  --repeat-count 100 \
  --input-length-range 128:256
```

### ShareGPT Dataset with Prefix Caching

```bash
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

python3 benchmarks/benchmark_prefix_caching.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \
  --enable-prefix-caching \
  --num-prompts 20 \
  --repeat-count 5 \
  --input-length-range 128:256
```

</details>

## ⚡ Example - Request Prioritization Benchmark

<details>
<summary>Show more</summary>

<br/>

Benchmark the performance of request prioritization in vLLM.

### Basic Prioritization Test

```bash
python3 benchmarks/benchmark_prioritization.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --input-len 128 \
  --output-len 64 \
  --num-prompts 100 \
  --scheduling-policy priority
```

### Multiple Sequences per Prompt

```bash
python3 benchmarks/benchmark_prioritization.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --input-len 128 \
  --output-len 64 \
  --num-prompts 100 \
  --scheduling-policy priority \
  --n 2
```

</details>

## 👁️ Example - Multi-Modal Benchmark

<details>
<summary>Show more</summary>

<br/>

Benchmark the performance of multi-modal requests in vLLM.

### Images (ShareGPT4V)

Start vLLM:

```bash
python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-VL-7B-Instruct \
  --dtype bfloat16 \
  --limit-mm-per-prompt '{"image": 1}' \
  --allowed-local-media-path /path/to/sharegpt4v/images
```

Send requests with images:

```bash
python benchmarks/benchmark_serving.py \
  --backend openai-chat \
  --model Qwen/Qwen2.5-VL-7B-Instruct \
  --dataset-name sharegpt \
  --dataset-path /path/to/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json \
  --num-prompts 100 \
  --save-result \
  --result-dir ~/vllm_benchmark_results \
  --save-detailed \
  --endpoint /v1/chat/completion
```

</details>