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vLLM & NVIDIA Triton User Meetup (Monday, September 9, 5pm-9pm PT) at Fort Mason, San Francisco

We are excited to announce our sixth vLLM Meetup, in collaboration with NVIDIA Triton Team.Join us to hear the vLLM's recent update about performance.Register nowhere and be part of the event!

Latest News 🔥

• [2024/07] We hostedthe fifth vLLM meetup with AWS! Please find the meetup slideshere.
• [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog posthere.
• [2024/06] We hostedthe fourth vLLM meetup with Cloudflare and BentoML! Please find the meetup slideshere.
• [2024/04] We hostedthe third vLLM meetup with Roblox! Please find the meetup slideshere.
• [2024/01] We hostedthe second vLLM meetup with IBM! Please find the meetup slideshere.
• [2023/10] We hostedthe first vLLM meetup with a16z! Please find the meetup slideshere.
• [2023/08] We would like to express our sincere gratitude toAndreessen 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 poweredLMSYS Vicuna and Chatbot Arena since mid-April. Check out ourblog post.

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 withPagedAttention
• Continuous batching of incoming requests
• Fast model execution with CUDA/HIP graph
• Quantizations:GPTQ,AWQ, INT4, INT8, and FP8.
• Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
• Speculative decoding
• Chunked prefill

Performance benchmark: We include aperformance benchmark that compares the performance of vLLM against other LLM serving engines (TensorRT-LLM,text-generation-inference andlmdeploy).

vLLM is flexible and easy to use with:

• Seamless integration with popular Hugging Face models
• High-throughput serving with various decoding algorithms, includingparallel sampling,beam search, and more
• Tensor parallelism and pipeline 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)
• Embedding Models (e.g. E5-Mistral)
• Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported modelshere.

Install vLLM withpip orfrom source:

pip install vllm

Visit ourdocumentation to learn more.

• Installation
• Quickstart
• Supported Models

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

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

• a16z
• AMD
• Anyscale
• AWS
• Crusoe Cloud
• Databricks
• DeepInfra
• Dropbox
• Google Cloud
• Lambda Lab
• NVIDIA
• Replicate
• Roblox
• RunPod
• Sequoia Capital
• Skywork AI
• Trainy
• UC Berkeley
• UC San Diego
• ZhenFund

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

If you use vLLM for your research, please cite ourpaper:

@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}}