# 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
Dataset Online Offline Data Path
ShareGPT ✅ ✅ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
BurstGPT ✅ ✅ wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
Sonnet ✅ ✅ Local file: benchmarks/sonnet.txt
Random ✅ ✅ synthetic
HuggingFace-VisionArena ✅ ✅ lmarena-ai/VisionArena-Chat
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
✅: supported 🟡: Partial support 🚧: to be supported **Note**: HuggingFace dataset's `dataset-name` should be set to `hf` --- ## Example - Online Benchmark First start serving your model ```bash vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests ``` 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 python3 vllm/benchmarks/benchmark_serving.py \ --backend vllm \ --model NousResearch/Hermes-3-Llama-3.1-8B \ --endpoint /v1/completions \ --dataset-name sharegpt \ --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ --num-prompts 10 ``` If successful, you will see the following output ``` ============ 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 ================================================== ``` ### VisionArena Benchmark for Vision Language Models ```bash # need a model with vision capability here vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests ``` ```bash python3 vllm/benchmarks/benchmark_serving.py \ --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 \ --ngram_prompt_lookup_min 2 \ --ngram-prompt-lookup-max 5 \ --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5} ``` ``` bash python3 benchmarks/benchmark_serving.py \ --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 --disable-log-requests ``` **`lmms-lab/LLaVA-OneVision-Data`** ```bash python3 vllm/benchmarks/benchmark_serving.py \ --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 python3 vllm/benchmarks/benchmark_serving.py \ --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 python3 vllm/benchmarks/benchmark_serving.py \ --model Qwen/QwQ-32B \ --dataset-name hf \ --dataset-path AI-MO/aimo-validation-aime \ --num-prompts 10 \ --seed 42 ``` ### Running With Sampling Parameters When using OpenAI-compatible backends such as `vllm`, optional sampling parameters can be specified. Example client command: ```bash python3 vllm/benchmarks/benchmark_serving.py \ --backend vllm \ --model NousResearch/Hermes-3-Llama-3.1-8B \ --endpoint /v1/completions \ --dataset-name sharegpt \ --dataset-path /ShareGPT_V3_unfiltered_cleaned_split.json \ --top-k 10 \ --top-p 0.9 \ --temperature 0.5 \ --num-prompts 10 ``` --- ## Example - Offline Throughput Benchmark ```bash python3 vllm/benchmarks/benchmark_throughput.py \ --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 ``` 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 python3 vllm/benchmarks/benchmark_throughput.py \ --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 ``` 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 \ python3 vllm/benchmarks/benchmark_throughput.py \ --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 \ --ngram_prompt_lookup_min=2 \ --ngram-prompt-lookup-max=5 \ --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5} ``` ``` 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 python3 vllm/benchmarks/benchmark_throughput.py \ --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 python3 vllm/benchmarks/benchmark_throughput.py \ --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 python3 benchmarks/benchmark_throughput.py \ --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 python3 vllm/benchmarks/benchmark_throughput.py \ --model meta-llama/Llama-2-7b-hf \ --backend vllm \ --dataset_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 ```