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vLLM

Easy, fast, and cheap LLM serving for everyone

| Documentation | Blog | Paper | Discord |

Latest News 🔥

  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.

About

vLLM is a fast and easy-to-use library for LLM inference and serving.

vLLM is fast with:

  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels

vLLM is flexible and easy to use with:

  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support

vLLM seamlessly supports many Hugging Face models, including the following architectures:

  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

Install vLLM with pip or from source:

pip install vllm

Getting Started

Visit our documentation to get started.

Contributing

We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.

Citation

If you use vLLM for your research, please cite our paper:

@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}

About

A high-throughput and memory-efficient inference and serving engine for LLMs

docs.vllm.ai

Resources

Readme
License

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Discord |

Latest News 🔥
  • [2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
  • [2024/01] Added ROCm 6.0 support to vLLM.
  • [2023/12] Added ROCm 5.7 support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels
vLLM is flexible and easy to use with:
  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support
vLLM seamlessly supports many Hugging Face models, including the following architectures:
  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B, allenai/OLMo-7B, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen2-7B-beta, Qwen/Qwen-7B-Chat-beta, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)
Install vLLM with pip or from source:
pip install vllm
Getting Started
Visit our documentation to get started.
Contributing
We welcome and value any contributions and collaborations.
Please check out CONTRIBUTING.md for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
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A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
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