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vLLM

Easy, fast, and cheap LLM serving for everyone

| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥

  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News

About

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

Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill

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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support

vLLM seamlessly supports most popular open-source models on HuggingFace, including:

  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported models here.

Getting Started

Install vLLM with pip or from source:

pip install vllm

Visit our documentation to learn more.

Contributing

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

Sponsors

vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!

Cash Donations:

  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund

Compute Resources:

  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego

Slack Sponsor: Anyscale

We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.

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}
}

Contact Us

Media Kit

About

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

docs.vllm.ai

Resources

Readme
License

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity

Stars

4 stars

Watchers

1 watching

Forks

2 forks
Report repository

Releases

Packages

Contributors

Languages

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Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
Media Kit
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
4 stars
Watchers
1 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
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Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
Media Kit
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
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setup.py
use_existing_torch.py
use_existing_torch.py
Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
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A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
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Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
Media Kit
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
4 stars
Watchers
1 watching
Forks
2 forks
Report repository
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, 'i');
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Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
Media Kit
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
4 stars
Watchers
1 watching
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2 forks
Report repository
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Repository files navigation
vLLM

Easy, fast, and cheap LLM serving for everyone


| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |

Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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}
}
Contact Us
Media Kit
About
A high-throughput and memory-efficient inference and serving engine for LLMs
docs.vllm.ai
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Stars
4 stars
Watchers
1 watching
Forks
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Report repository
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, 'i');
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// Universal Dark Mode - works on any site
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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vLLM

Easy, fast, and cheap LLM serving for everyone


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Latest News 🔥
  • [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides here.
  • [2025/08] We hosted vLLM Beijing Meetup focusing on large-scale LLM deployment! Please find the meetup slides here and the recording here.
  • [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement here.
  • [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post here.
Previous News
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
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
  • Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
  • Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
  • Speculative decoding
  • Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
  • Prefix caching support
  • Multi-LoRA support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
  • Transformer-like LLMs (e.g., Llama)
  • Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
  • Embedding Models (e.g., E5-Mistral)
  • Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models here.
Getting Started
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations.
Please check out Contributing to vLLM for how to get involved.
Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
Cash Donations:
  • a16z
  • Dropbox
  • Sequoia Capital
  • Skywork AI
  • ZhenFund
Compute Resources:
  • Alibaba Cloud
  • AMD
  • Anyscale
  • AWS
  • Crusoe Cloud
  • Databricks
  • DeepInfra
  • Google Cloud
  • Intel
  • Lambda Lab
  • Nebius
  • Novita AI
  • NVIDIA
  • Replicate
  • Roblox
  • RunPod
  • Trainy
  • UC Berkeley
  • UC San Diego
Slack Sponsor: Anyscale
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
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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