Easy, fast, and cheap LLM serving for everyone

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Join us at the PyTorch Conference, October 22-23 and Ray Summit, November 3-5 in San Francisco for our latest updates on vLLM and to meet the vLLM team! Register now for the largest vLLM community events of the year!

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vLLM for Windows

vLLM for Windows build & kernels. This repository will be updated when new versions of vLLM are released.

Don't open a new Issue to request a specific commit build. Wait for a new stable release.

Don't open Issues for general vLLM questions or non Windows related problems. Only Windows specific issues. Any Issue opened that is not Windows specific will be closed automatically.

Don't request a wheel for your specific environment. If your environment does not match the released wheel, build your own wheel from source by following the instructions below.

Windows instructions:

Installing an existing release wheel:

1. Ensure that you have the correct Python, Torch and CUDA version of the wheel. The Python, Torch and CUDA version of the wheel is specified in the release version.
2. Download the wheel from the release version of your preference (latest wheel here).
3. Install it with pip install DOWNLOADED_WHEEL_PATH

Building from source:

A Visual Studio 2019 or newer is required to launch the compiler x64 environment. The installation path is referred in the instructions as VISUAL_STUDIO_INSTALL_PATH.

CUDA path will be found automatically if you have the bin folder in your PATH, or have the CUDA installation path settled on well-known environment vars like CUDA_ROOT, CUDA_HOME or CUDA_PATH.

If none of these are present, make sure to set the environment variable before starting the build: set CUDA_ROOT=CUDA_INSTALLATION_PATH

1. Open a Command Line (cmd.exe)
2. Clone the vLLM repository: cd C:\ & git clone https://github.com/SystemPanic/vllm-windows.git
3. Execute (in cmd) VISUAL_STUDIO_INSTALL_PATH\VC\Auxiliary\Build\vcvarsall.bat x64
4. Change the working directory to the cloned repository path, for example: cd C:\vllm-windows
5. Set the following environment variables:

set DISTUTILS_USE_SDK=1
set VLLM_TARGET_DEVICE=cuda
#(replace 10 with your desired cpu threads to use in parallel to speed up compilation)
set MAX_JOBS=10

#Optional variables:

#To include cuDSS (only if you have cuDSS installed)
set USE_CUDSS=1
set CUDSS_LIBRARY_PATH=PATH_TO_CUDSS_INSTALL_DIR\lib\12
set CUDSS_INCLUDE_PATH=PATH_TO_CUDSS_INSTALL_DIR\include

#To include cuSPARSELt (only if you have cuSPARSELt installed)
set USE_CUSPARSELT=1
set CUSPARSELT_INCLUDE_PATH=PATH_TO_CUSPARSELT_INSTALL_DIR\include
set CUSPARSELT_LIBRARY_PATH=PATH_TO_CUSPARSELT_INSTALL_DIR\lib

#To include cuDNN:
set USE_CUDNN=1
set CUDNN_LIBRARY_PATH=PATH_TO_CUDNN_INSTALL_DIR\lib\CUDNN_CUDA_VERSION\x64
set CUDNN_INCLUDE_PATH=PATH_TO_CUDNN_INSTALL_DIR\include\CUDNN_CUDA_VERSION

#Flash Attention v3 build has been disabled inside WSL2 and Windows due to compiler being killed on WSL2, and extremely long compiling times on Windows. Hopper is not available on Windows, so FA3 has no sense anyway. 
#Build can be forcefully enabled using the following environment var:
set VLLM_FORCE_FA3_WINDOWS_BUILD=1

6. Build & install:

#With torch 2.7.1 cuda 12.6 (change cu126 with your installed CUDA version)
pip install torch==2.7.1+cu126 torchaudio==2.7.1+cu126 torchvision==0.22.1+cu126 --index-url https://download.pytorch.org/whl/cu126

#With your already installed torch cuda version (make sure you have torch cuda installed if you use a virtual environment)
python use_existing_torch.py

pip install -r requirements/build.txt
pip install -r requirements/windows.txt
pip install . --no-build-isolation

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Latest News 🔥

• [2025/08] We hosted vLLM Shenzhen Meetup focusing on the ecosystem around vLLM! Please find the meetup slides here.
• [2025/08] We hosted vLLM Singapore Meetup. We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides here.
• [2025/08] We hosted vLLM Shanghai Meetup focusing on building, developing, and integrating with vLLM! Please find the meetup slides 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

• [2025/08] We hosted vLLM Korea Meetup with Red Hat and Rebellions! We shared the latest advancements in vLLM along with project spotlights from the vLLM Korea community. 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] We hosted NYC vLLM Meetup! Please find the meetup slides here.
• [2025/04] We hosted Asia Developer Day! Please find the meetup slides from the vLLM team here.
• [2025/03] We hosted vLLM x Ollama Inference Night! Please find the meetup slides from the vLLM team here.
• [2025/03] We hosted the first vLLM China Meetup! Please find the meetup slides from vLLM team here.
• [2025/03] We hosted the East Coast vLLM Meetup! Please find the meetup slides here.
• [2025/02] We hosted the ninth vLLM meetup with Meta! Please find the meetup slides from vLLM team here and AMD here. The slides from Meta will not be posted.
• [2025/01] We hosted the eighth vLLM meetup with Google Cloud! Please find the meetup slides from vLLM team here, and Google Cloud team here.
• [2024/12] vLLM joins pytorch ecosystem! Easy, Fast, and Cheap LLM Serving for Everyone!
• [2024/11] We hosted the seventh vLLM meetup with Snowflake! Please find the meetup slides from vLLM team here, and Snowflake team here.
• [2024/10] We have just created a developer slack (slack.vllm.ai) focusing on coordinating contributions and discussing features. Please feel free to join us there!
• [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team here. Learn more from the talks from other vLLM contributors and users!
• [2024/09] We hosted the sixth vLLM meetup with NVIDIA! Please find the meetup slides here.
• [2024/07] We hosted the fifth vLLM meetup with AWS! Please find the meetup slides here.
• [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post here.
• [2024/06] We hosted the fourth vLLM meetup with Cloudflare and BentoML! Please find the meetup slides here.
• [2024/04] We hosted the third vLLM meetup with Roblox! Please find the meetup slides here.
• [2024/01] We hosted the second vLLM meetup with IBM! Please find the meetup slides here.
• [2023/10] We hosted the first vLLM meetup with a16z! Please find the meetup slides here.
• [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.
• [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.

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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.

• Installation
• Quickstart
• List of Supported Models

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

• For technical questions and feature requests, please use GitHub Issues
• For discussing with fellow users, please use the vLLM Forum
• For coordinating contributions and development, please use Slack
• For security disclosures, please use GitHub's Security Advisories feature
• For collaborations and partnerships, please contact us at vllm-questions@lists.berkeley.edu

Media Kit

• If you wish to use vLLM's logo, please refer to our media kit repo