Benchmark CLI

This section 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.

!!! tip The benchmarks described on this page are mainly for evaluating specific vLLM features as well as regression testing.

For benchmarking production vLLM servers, we recommend [GuideLLM](https://github.com/vllm-project/guidellm), an established performance benchmarking framework with live progress updates and automatic report generation. It is also more flexible than `vllm bench serve` in terms of dataset loading, request formatting, and workload patterns.

Dataset Overview

Dataset	Online	Offline	Data Path
ShareGPT	✅	✅	`wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`
ShareGPT4V (Image)	✅	✅	`wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json`
Note that the images need to be downloaded separately. For example, to download COCO's 2017 Train images:
`wget http://images.cocodataset.org/zips/train2017.zip`
ShareGPT4Video (Video)	✅	✅	`git clone https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video`
BurstGPT	✅	✅	`wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv`
Sonnet (deprecated)	✅	✅	Local file: `benchmarks/sonnet.txt`
Random	✅	✅	`synthetic`
RandomMultiModal (Image/Video)	✅	✅	`synthetic`
RandomForReranking	✅	✅	`synthetic`
Prefix Repetition	✅	✅	`synthetic`
HuggingFace-VisionArena	✅	✅	`lmarena-ai/VisionArena-Chat`
HuggingFace-MMVU	✅	✅	`yale-nlp/MMVU`
HuggingFace-InstructCoder	✅	✅	`likaixin/InstructCoder`
HuggingFace-AIMO	✅	✅	`AI-MO/aimo-validation-aime`, `AI-MO/NuminaMath-1.5`, `AI-MO/NuminaMath-CoT`
HuggingFace-Other	✅	✅	`lmms-lab/LLaVA-OneVision-Data`, `Aeala/ShareGPT_Vicuna_unfiltered`
HuggingFace-MTBench	✅	✅	`philschmid/mt-bench`
HuggingFace-Blazedit	✅	✅	`vdaita/edit_5k_char`, `vdaita/edit_10k_char`
HuggingFace-ASR	✅	✅	`openslr/librispeech_asr`, `facebook/voxpopuli`, `LIUM/tedlium`, `edinburghcstr/ami`, `speechcolab/gigaspeech`, `kensho/spgispeech`
Spec Bench	✅	✅	`wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl`
SPEED-Bench	✅	✅	`curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -`
Custom	✅	✅	Local file: `data.jsonl`
Custom MM	✅	✅	Local file: `mm_data.jsonl`

Legend:

✅ - supported
🟡 - Partial support
🚧 - to be supported

!!! note HuggingFace dataset's dataset-name should be set to hf. For local dataset-path, please set hf-name to its Hugging Face ID like

```bash
--dataset-path /datasets/VisionArena-Chat/ --hf-name lmarena-ai/VisionArena-Chat
```

Examples

🚀 Online Benchmark

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
==================================================

Results Visualization

The --plot-timeline and --plot-dataset-stats can be used to generate respectively the requests completion timeline and dataset prompt and output tokens statistics, which can be useful for debugging purpose or for deeper analysis.

bash

vllm bench serve \
    --backend vllm \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --endpoint /v1/completions \
    --dataset-name sharegpt \
    --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
    --num-prompts 100 \
    --plot-timeline \
    --timeline-itl-thresholds 2,5 \
    --plot-dataset-stats \
    --save-result

Interactive Timeline

The generated timeline is an interactive visualization in the form of an HTML file that can be rendered in most browsers. To customize the ITL color thresholds, one can use --timeline-itl-thresholds flag (default: 25ms, 50ms)

Example output:

Dataset statistics

The generated figure shows the input prompt and output tokens distribution.

Example output:

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 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.

Custom multimodal dataset

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

json

{"prompt": "How many animals are present in the given image?", "image_files": ["/path/to/image/folder/horsepony.jpg"]}
{"prompt": "What colour is the bird shown in the image?", "image_files": ["/path/to/image/folder/flycatcher.jpeg"]}

bash

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

bash

# run benchmarking script
vllm bench serve--save-result --save-detailed \
  --backend openai-chat \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name custom_mm \
  --dataset-path <path-to-your-mm-data-jsonl> \
  --allowed-local-media-path /path/to/image/folder

Note that we need to use the openai-chat backend and /v1/chat/completions endpoint for multimodal inputs.

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 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

Spec Bench Benchmark with Speculative Decoding

bash

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}'

SpecBench dataset

Run all categories:

bash

# Download the dataset using:
# wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl

vllm bench serve \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset-name spec_bench \
    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/spec_bench/question.jsonl" \
    --num-prompts -1

Available categories include [writing, roleplay, reasoning, math, coding, extraction, stem, humanities, translation, summarization, qa, math_reasoning, rag].

Run only a specific category like "summarization":

bash

vllm bench serve \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset-name spec_bench \
    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/spec_bench/question.jsonl" \
    --num-prompts -1
    --spec-bench-category "summarization"

SPEED-Bench Benchmark with Speculative Decoding

SPEED-Bench is a unified and diverse dataset for speculative decoding, supporting acceptance rate and length measurements using the Qualitative split and throughput measurements using the Throughput splits in 5 configuration of input sequence length (1k, 2k, 8k, 16k, 32k).

!!! note This dataset is governed by the NVIDIA Evaluation Dataset License Agreement. For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose. The prepare.py script automatically fetches data from all the source datasets.

First, download the dataset to a folder, using this one liner:

bash

curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -

The command supports also the following arguments:

--config: download only a subset of the dataset: qualitative, throughput_1k, throughput_2k, throughput_8k, throughput_16k and throughput_32k. By default, it will download all subsets.
--output_dir: download to a specified folder. By default, it will download to the current directory.

Start a server with speculative decoding:

bash

vllm serve meta-llama/Llama-3.3-70B-Instruct \
    --speculative-config $'{"method": "eagle3",
    "num_speculative_tokens": 3,
    "model": "nvidia/Llama-3.3-70B-Instruct-Eagle3"}'

Run all categories in the Qualitative split:

bash

vllm bench serve \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --dataset-name speed_bench \
    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
    --num-prompts -1

Available categories include [writing, roleplay, reasoning, math, coding, stem, humanities, multilingual, summarization, qa, rag].

Run only a specific category like "multilingual":

bash

vllm bench serve \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --dataset-name speed_bench \
    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
    --num-prompts -1
    --speed-bench-category "multilingual"

Run all categories in the Throughput split (2k ISL):

bash

vllm bench serve \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --dataset-name speed_bench \
    --speed-bench-dataset-subset throughput_2k
    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench/" \
    --num-prompts -1

Available categories include [high_entropy, mixed, low_entropy], where high entropy data contains unstructued data such as creative writing while low entropy data contains more structured data such as coding, more details are in the dataset card.

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

vdaita/edit_5k_char or vdaita/edit_10k_char:

bash

vllm bench serve \
    --model Qwen/QwQ-32B \
    --dataset-name hf \
    --dataset-path vdaita/edit_5k_char \
    --num-prompts 90 \
    --blazedit-min-distance 0.01 \
    --blazedit-max-distance 0.99

openslr/librispeech_asr, facebook/voxpopuli, LIUM/tedlium, edinburghcstr/ami, speechcolab/gigaspeech, kensho/spgispeech

bash

vllm bench serve \
    --model openai/whisper-large-v3-turbo \
    --backend openai-audio \
    --dataset-name hf \
    --dataset-path facebook/voxpopuli --hf-subset en --hf-split test --no-stream --trust-remote-code \
    --num-prompts 99999999 \
    --no-oversample \
    --endpoint /v1/audio/transcriptions \
    --ready-check-timeout-sec 600 \
    --save-result \
    --max-concurrency 512

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.

Load Pattern Configuration

vLLM's benchmark serving script provides sophisticated load pattern simulation capabilities through three key parameters that control request generation and concurrency behavior:

Load Pattern Control Parameters

--request-rate: Controls the target request generation rate (requests per second). Set to inf for maximum throughput testing or finite values for controlled load simulation.
--burstiness: Controls traffic variability using a Gamma distribution (range: > 0). Lower values create bursty traffic, higher values create uniform traffic.
--max-concurrency: Limits concurrent outstanding requests. If this argument is not provided, concurrency is unlimited. Set a value to simulate backpressure.

These parameters work together to create realistic load patterns with carefully chosen defaults. The --request-rate parameter defaults to inf (infinite), which sends all requests immediately for maximum throughput testing. When set to finite values, it uses either a Poisson process (default --burstiness=1.0) or Gamma distribution for realistic request timing. The --burstiness parameter only takes effect when --request-rate is not infinite - a value of 1.0 creates natural Poisson traffic, while lower values (0.1-0.5) create bursty patterns and higher values (2.0-5.0) create uniform spacing. The --max-concurrency parameter defaults to None (unlimited) but can be set to simulate real-world constraints where a load balancer or API gateway limits concurrent connections. When combined, these parameters allow you to simulate everything from unrestricted stress testing (--request-rate=inf) to production-like scenarios with realistic arrival patterns and resource constraints.

The --burstiness parameter mathematically controls request arrival patterns using a Gamma distribution where:

Shape parameter: burstiness value
Coefficient of Variation (CV): $\frac{1}{\sqrt{burstiness}}$
Traffic characteristics:
- burstiness = 0.1: Highly bursty traffic (CV ≈ 3.16) - stress testing
- burstiness = 1.0: Natural Poisson traffic (CV = 1.0) - realistic simulation
- burstiness = 5.0: Uniform traffic (CV ≈ 0.45) - controlled load testing

Figure: Load pattern examples for each use case. Top row: Request arrival timelines showing cumulative requests over time. Bottom row: Inter-arrival time distributions showing traffic variability patterns. Each column represents a different use case with its specific parameter settings and resulting traffic characteristics.

Load Pattern Recommendations by Use Case:

Use Case	Burstiness	Request Rate	Max Concurrency	Description
Maximum Throughput	N/A	Infinite	Limited	Most common: Simulates load balancer/gateway limits with unlimited user demand
Realistic Testing	1.0	Moderate (5-20)	Infinite	Natural Poisson traffic patterns for baseline performance
Stress Testing	0.1-0.5	High (20-100)	Infinite	Challenging burst patterns to test resilience
Latency Profiling	2.0-5.0	Low (1-10)	Infinite	Uniform load for consistent timing analysis
Capacity Planning	1.0	Variable	Limited	Test resource limits with realistic constraints
SLA Validation	1.0	Target rate	SLA limit	Production-like constraints for compliance testing

These load patterns help evaluate different aspects of your vLLM deployment, from basic performance characteristics to resilience under challenging traffic conditions.

The Maximum Throughput pattern (--request-rate=inf --max-concurrency=<limit>) is the most commonly used configuration for production benchmarking. This simulates real-world deployment architectures where:

Users send requests as fast as they can (infinite rate)
A load balancer or API gateway controls the maximum concurrent connections
The system operates at its concurrency limit, revealing true throughput capacity
--burstiness has no effect since request timing is not controlled when rate is infinite

This pattern helps determine optimal concurrency settings for your production load balancer configuration.

To effectively configure load patterns, especially for Capacity Planning and SLA Validation use cases, you need to understand your system's resource limits. During startup, vLLM reports KV cache configuration that directly impacts your load testing parameters:

text

GPU KV cache size: 15,728,640 tokens
Maximum concurrency for 8,192 tokens per request: 1920

Where:

GPU KV cache size: Total tokens that can be cached across all concurrent requests
Maximum concurrency: Theoretical maximum concurrent requests for the given max_model_len
Calculation: max_concurrency = kv_cache_size / max_model_len

Using KV cache metrics for load pattern configuration:

For Capacity Planning: Set --max-concurrency to 80-90% of the reported maximum to test realistic resource constraints
For SLA Validation: Use the reported maximum as your SLA limit to ensure compliance testing matches production capacity
For Realistic Testing: Monitor memory usage when approaching theoretical limits to understand sustainable request rates
Request rate guidance: Use the KV cache size to estimate sustainable request rates for your specific workload and sequence lengths

</details>

📈 Offline Throughput Benchmark

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 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

Synthetic Random Multimodal (random-mm)

Generate synthetic multimodal inputs for offline throughput testing without external datasets. Use --backend vllm-chat so that image tokens are counted correctly.

bash

vllm bench throughput \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --backend vllm-chat \
  --dataset-name random-mm \
  --num-prompts 100 \
  --random-input-len 300 \
  --random-output-len 40 \
  --random-mm-base-items-per-request 2 \
  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}'

</details>

🛠️ Structured Output Benchmark

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>

📚 Long Document QA Benchmark

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>

🗂️ Prefix Caching Benchmark

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

Prefix Repetition Dataset

bash

vllm bench serve \
  --backend openai \
  --model meta-llama/Llama-2-7b-chat-hf \
  --dataset-name prefix_repetition \
  --num-prompts 100 \
  --prefix-repetition-prefix-len 512 \
  --prefix-repetition-suffix-len 128 \
  --prefix-repetition-num-prefixes 5 \
  --prefix-repetition-output-len 128

</details>

🧪 Hashing Benchmarks

Two helper scripts live in benchmarks/ to compare hashing options used by prefix caching and related utilities. They are standalone (no server required) and help choose a hash algorithm before enabling prefix caching in production.

benchmarks/benchmark_hash.py: Micro-benchmark that measures per-call latency of three implementations on a representative (bytes, tuple[int]) payload.

bash

python benchmarks/benchmark_hash.py --iterations 20000 --seed 42

benchmarks/benchmark_prefix_block_hash.py: End-to-end block hashing benchmark that runs the full prefix-cache hash pipeline (hash_block_tokens) across many fake blocks and reports throughput.

bash

python benchmarks/benchmark_prefix_block_hash.py --num-blocks 20000 --block-size 32 --trials 5

Supported algorithms: sha256, sha256_cbor, xxhash, xxhash_cbor. Install optional deps to exercise all variants:

bash

uv pip install xxhash cbor2

If an algorithm’s dependency is missing, the script will skip it and continue.

</details>

⚡ Request Prioritization Benchmark

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> <details class="admonition abstract" markdown="1"> <summary>Show more</summary>

Benchmark the performance of multi-modal requests in vLLM.

Images (ShareGPT4V)

Start vLLM:

bash

vllm serve 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

vllm bench serve \
  --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/completions

Videos (ShareGPT4Video)

Start vLLM:

bash

vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
  --dtype bfloat16 \
  --limit-mm-per-prompt '{"video": 1}' \
  --allowed-local-media-path /path/to/sharegpt4video/videos

Send requests with videos:

bash

vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-VL-7B-Instruct \
  --dataset-name sharegpt \
  --dataset-path /path/to/ShareGPT4Video/llava_v1_5_mix665k_with_video_chatgpt72k_share4video28k.json \
  --num-prompts 100 \
  --save-result \
  --result-dir ~/vllm_benchmark_results \
  --save-detailed \
  --endpoint /v1/chat/completions

Synthetic Random Images (random-mm)

Generate synthetic image inputs alongside random text prompts to stress-test vision models without external datasets.

Notes:

For online benchmarks, use --backend openai-chat with endpoint /v1/chat/completions.
For offline benchmarks, use --backend vllm-chat (see Offline Throughput Benchmark for an example).

Start the server (example):

bash

vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
  --dtype bfloat16 \
  --max-model-len 16384 \
  --limit-mm-per-prompt '{"image": 3, "video": 0}' \
  --mm-processor-kwargs max_pixels=1003520

Benchmark. It is recommended to use the flag --ignore-eos to simulate real responses. You can set the size of the output via the arg random-output-len.

Ex.1: Fixed number of items and a single image resolution, enforcing generation of approx 40 tokens:

bash

vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-VL-3B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random-mm \
  --num-prompts 100 \
  --max-concurrency 10 \
  --random-prefix-len 25 \
  --random-input-len 300 \
  --random-output-len 40 \
  --random-range-ratio 0.2 \
  --random-mm-base-items-per-request 2 \
  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
  --random-mm-bucket-config '{(224, 224, 1): 1.0}' \
  --request-rate inf \
  --ignore-eos \
  --seed 42

The number of items per request can be controlled by passing multiple image buckets:

bash

  --random-mm-base-items-per-request 2 \
  --random-mm-num-mm-items-range-ratio 0.5 \
  --random-mm-limit-mm-per-prompt '{"image": 4, "video": 0}' \
  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}' \

Flags specific to random-mm:

--random-mm-base-items-per-request: base number of multimodal items per request.
--random-mm-num-mm-items-range-ratio: vary item count uniformly in the closed integer range [floor(n·(1−r)), ceil(n·(1+r))]. Set r=0 to keep it fixed; r=1 allows 0 items.
--random-mm-limit-mm-per-prompt: per-modality hard caps, e.g. '{"image": 3, "video": 0}'.
--random-mm-bucket-config: dict mapping (H, W, T) → probability. Entries with probability 0 are removed; remaining probabilities are renormalized to sum to 1. Use T=1 for images. Set any T>1 for videos (video sampling not yet supported).

Behavioral notes:

If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping.

How sampling works:

Determine per-request item count k by sampling uniformly from the integer range defined by --random-mm-base-items-per-request and --random-mm-num-mm-items-range-ratio, then clamp k to at most the sum of per-modality limits.
For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in --random-mm-bucket-config, while tracking how many items of each modality have been added.
If a modality (e.g., image) reaches its limit from --random-mm-limit-mm-per-prompt, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing. This should be seen as an edge case, and if this behavior can be avoided by setting --random-mm-limit-mm-per-prompt to a large number. Note that this might result in errors due to engine config --limit-mm-per-prompt.
The resulting request contains synthetic image data in multi_modal_data (OpenAI Chat format). When random-mm is used with the OpenAI Chat backend, prompts remain text and MM content is attached via multi_modal_data.

</details>

🔬 Multimodal Processor Benchmark

Benchmark per-stage latency of the multimodal (MM) input processor pipeline, including the encoder forward pass. This is useful for profiling preprocessing bottlenecks in vision-language models.

The benchmark measures the following stages for each request:

Stage	Description
`get_mm_hashes_secs`	Time spent hashing multimodal inputs
`get_cache_missing_items_secs`	Time spent looking up the processor cache
`apply_hf_processor_secs`	Time spent in the HuggingFace processor
`merge_mm_kwargs_secs`	Time spent merging multimodal kwargs
`apply_prompt_updates_secs`	Time spent updating prompt tokens
`preprocessor_total_secs`	Total preprocessing time
`encoder_forward_secs`	Time spent in the encoder model forward pass
`num_encoder_calls`	Number of encoder invocations per request

The benchmark also reports end-to-end latency (TTFT + decode time) per request. Use --metric-percentiles to select which percentiles to report (default: p99) and --output-json to save results.

Basic Example with Synthetic Data (random-mm)

bash

vllm bench mm-processor \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --dataset-name random-mm \
  --num-prompts 50 \
  --random-input-len 300 \
  --random-output-len 40 \
  --random-mm-base-items-per-request 2 \
  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}'

Using a HuggingFace Dataset

bash

vllm bench mm-processor \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --dataset-name hf \
  --dataset-path lmarena-ai/VisionArena-Chat \
  --hf-split train \
  --num-prompts 100

Warmup, Custom Percentiles, and JSON Output

bash

vllm bench mm-processor \
  --model Qwen/Qwen2-VL-7B-Instruct \
  --dataset-name random-mm \
  --num-prompts 200 \
  --num-warmups 5 \
  --random-input-len 300 \
  --random-output-len 40 \
  --random-mm-base-items-per-request 1 \
  --metric-percentiles 50,90,95,99 \
  --output-json results.json

See vllm bench mm-processor for the full argument reference.

</details>

Embedding Benchmark

Benchmark the performance of embedding requests in vLLM.

Text Embeddings

Unlike generative models which use Completions API or Chat Completions API, you should set --backend openai-embeddings and --endpoint /v1/embeddings to use the Embeddings API.

You can use any text dataset to benchmark the model, such as ShareGPT.

Start the server:

bash

vllm serve jinaai/jina-embeddings-v3 --trust-remote-code

Run the benchmark:

bash

# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
vllm bench serve \
  --model jinaai/jina-embeddings-v3 \
  --backend openai-embeddings \
  --endpoint /v1/embeddings \
  --dataset-name sharegpt \
  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json

Multi-modal Embeddings

Unlike generative models which use Completions API or Chat Completions API, you should set --endpoint /v1/embeddings to use the Embeddings API. The backend to use depends on the model:

CLIP: --backend openai-embeddings-clip
VLM2Vec: --backend openai-embeddings-vlm2vec

For other models, please add your own implementation inside vllm/benchmarks/lib/endpoint_request_func.py to match the expected instruction format.

You can use any text or multi-modal dataset to benchmark the model, as long as the model supports it. For example, you can use ShareGPT and VisionArena to benchmark vision-language embeddings.

Serve and benchmark CLIP:

bash

# Run this in another process
vllm serve openai/clip-vit-base-patch32

# Run these one by one after the server is up
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
vllm bench serve \
  --model openai/clip-vit-base-patch32 \
  --backend openai-embeddings-clip \
  --endpoint /v1/embeddings \
  --dataset-name sharegpt \
  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json

vllm bench serve \
  --model openai/clip-vit-base-patch32 \
  --backend openai-embeddings-clip \
  --endpoint /v1/embeddings \
  --dataset-name hf \
  --dataset-path lmarena-ai/VisionArena-Chat

Serve and benchmark VLM2Vec:

bash

# Run this in another process
vllm serve TIGER-Lab/VLM2Vec-Full --runner pooling \
  --trust-remote-code \
  --chat-template examples/template_vlm2vec_phi3v.jinja

# Run these one by one after the server is up
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
vllm bench serve \
  --model TIGER-Lab/VLM2Vec-Full \
  --backend openai-embeddings-vlm2vec \
  --endpoint /v1/embeddings \
  --dataset-name sharegpt \
  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json

vllm bench serve \
  --model TIGER-Lab/VLM2Vec-Full \
  --backend openai-embeddings-vlm2vec \
  --endpoint /v1/embeddings \
  --dataset-name hf \
  --dataset-path lmarena-ai/VisionArena-Chat

</details>

Reranker Benchmark

Benchmark the performance of rerank requests in vLLM.

Unlike generative models which use Completions API or Chat Completions API, you should set --backend vllm-rerank and --endpoint /v1/rerank to use the Reranker API.

For reranking, the only supported dataset is --dataset-name random-rerank

Start the server:

bash

vllm serve BAAI/bge-reranker-v2-m3

Run the benchmark:

bash

vllm bench serve \
  --model BAAI/bge-reranker-v2-m3 \
  --backend vllm-rerank \
  --endpoint /v1/rerank \
  --dataset-name random-rerank \
  --tokenizer BAAI/bge-reranker-v2-m3 \
  --random-input-len 512 \
  --num-prompts 10 \
  --random-batch-size 5

For reranker models, this will create num_prompts / random_batch_size requests with random_batch_size "documents" where each one has close to random_input_len tokens. In the example above, this results in 2 rerank requests with 5 "documents" each where each document has close to 512 tokens.

Please note that the /v1/rerank is also supported by embedding models. So if you're running with an embedding model, also set --no_reranker. Because in this case the query is treated as an individual prompt by the server, here we send random_batch_size - 1 documents to account for the extra prompt which is the query. The token accounting to report the throughput numbers correctly is also adjusted.

</details>

Benchmark CLI

Benchmark CLI

Dataset Overview

Examples

🚀 Online Benchmark

Results Visualization

Interactive Timeline

Dataset statistics

Custom Dataset

Custom multimodal dataset

VisionArena Benchmark for Vision Language Models

InstructCoder Benchmark with Speculative Decoding

Spec Bench Benchmark with Speculative Decoding

SPEED-Bench Benchmark with Speculative Decoding

Other HuggingFaceDataset Examples

Running With Sampling Parameters

Running With Ramp-Up Request Rate

Load Pattern Configuration

Load Pattern Control Parameters

📈 Offline Throughput Benchmark

VisionArena Benchmark for Vision Language Models

InstructCoder Benchmark with Speculative Decoding

Other HuggingFaceDataset Examples

Synthetic Random Multimodal (random-mm)

🛠️ Structured Output Benchmark

Server Setup

JSON Schema Benchmark

Grammar-based Generation Benchmark

Regex-based Generation Benchmark

Choice-based Generation Benchmark

XGrammar Benchmark Dataset

📚 Long Document QA Benchmark

Basic Long Document QA Test

Different Repeat Modes

🗂️ Prefix Caching Benchmark

Fixed Prompt with Prefix Caching

ShareGPT Dataset with Prefix Caching

Prefix Repetition Dataset

🧪 Hashing Benchmarks

⚡ Request Prioritization Benchmark

Basic Prioritization Test

Multiple Sequences per Prompt

👁️ Multi-Modal Benchmark

Images (ShareGPT4V)

Videos (ShareGPT4Video)

Synthetic Random Images (random-mm)

🔬 Multimodal Processor Benchmark

Basic Example with Synthetic Data (random-mm)

Using a HuggingFace Dataset

Warmup, Custom Percentiles, and JSON Output

Embedding Benchmark

Text Embeddings

Multi-modal Embeddings

Reranker Benchmark