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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 for NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, Arm CPUs, and TPU. Additionally, support for diverse hardware plugins such as Intel Gaudi, IBM Spyre and Huawei Ascend.
- 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.
Install vLLM with pip or from source:
pip install vllmVisit our documentation to learn more.
The following priority scoring rules are used by the vLLM PR Dashboard to triage and rank pull requests.
Labels: rocm, amd, hip
Keywords: ROCm, AMD, HIP, MI200, MI210, MI250, MI300, MI300X, MI308, MI325X, MI350, MI355X, gfx90a, gfx940, gfx941, gfx942, gfx1100, gfx1101, gfx1200, gfx1201, hipblaslt, hipblas, rocblas, triton-rocm, AITER
Tracked Authors: sunway513, jataylo, autra, hongxiayang, ROCmSupport, scxiao, carlushuang, pruthvistony, jithunnair-amd, jerryyin, xinyazhang, liangan1, KKBankol, jglaser, tjtanaa, danieltahara, chuanqiw, ashvindhawan
Tracked Email Domains: amd.com
Keywords in PR titles/bodies that signal urgent issues and boost priority score:
| Keyword | Priority Score |
|---|---|
| data corruption | 30 |
| regression | 25 |
| crash | 25 |
| broken | 25 |
| segfault | 25 |
| hang | 20 |
| deadlock | 20 |
| SIGABRT | 20 |
| urgent | 20 |
| critical | 20 |
| hotfix | 20 |
| OOM | 15 |
| out of memory | 15 |
| does not work | 15 |
| failing | 10 |
PRs mentioning these organizations receive a priority boost:
| Customer | Priority Score |
|---|---|
| Meta | 30 |
| 30 | |
| AWS | 30 |
| Amazon | 30 |
| Anthropic | 30 |
| SemiAnalysis | 30 |
| Microsoft | 25 |
| 25 | |
| Databricks | 25 |
| Anyscale | 25 |
| Oracle | 20 |
| IBM | 20 |
| Intel | 15 |
| Samsung | 15 |
| Hugging Face | 15 |
High-importance code areas. Changes touching these paths receive a priority boost (scores adjusted +1 from baseline):
| Path / Area | Priority Score |
|---|---|
| attention | 16 |
| flash_attn | 16 |
| paged_attention | 16 |
| quantization | 16 |
| model_executor | 11 |
| model_runner | 11 |
| serving | 11 |
| scheduler | 11 |
| tensor_parallel | 11 |
| pipeline_parallel | 11 |
| distributed | 11 |
| cuda_graph | 11 |
| performance | 11 |
| benchmark | 6 |
We welcome and value any contributions and collaborations. Please check out Contributing to vLLM for how to get involved.
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}
}- 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 collaboration@vllm.ai
- If you wish to use vLLM's logo, please refer to our media kit repo