README

Easy, fast, and cheap LLM serving for everyone



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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 grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors.

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, chunked prefill, prefix caching
- Fast and flexible model execution with piecewise and full CUDA/HIP graphs
- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and more
- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton
- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL
- Speculative decoding including n-gram, suffix, EAGLE, DFlash
- Automatic kernel generation and graph-level transformations using torch.compile
- Disaggregated prefill, decode, and encode

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, expert, and context parallelism for distributed inference
- Streaming outputs
- Generation of structured outputs using xgrammar or guidance
- Tool calling and reasoning parsers
- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
- Efficient multi-LoRA support for dense and MoE layers
- Support for NVIDIA GPUs, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, Iluvatar CoreX GPU, and more.

vLLM seamlessly supports 200+ model architectures on Hugging Face, including:

- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)
- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5)
- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral)
- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT)
- Reward and classification models (e.g., Qwen-Math)

Find the full list of supported models here.

 Getting Started

Install vLLM with uv (recommended) or pip:

bash
uv pip install vllm
vllm download-kernels   precompiled kernels for faster startup


Or build from source for development.

Visit our documentation to learn more.

- Installation
- Quickstart
- List of Supported Models

 Contributing

We welcome and valu

Top issues

Discussions

Latest release

v0.33.0

 Highlights

This release features 927 commits from 389 contributors (122 new)!

 New models: Reflection Beam-1.0 (501B MoE, 23B active) with its DFlash2 drafter (62074), Yandex AliceAI-Foundation-80B-A3B (57963), Recursive Flint (40B MoE, 2.5B active) (62938), Muse Spark Open (61879), Step-5 Preview (61727, 63147), NVIDIA Alpamayo 2 Super (62957), Nemotron-Labs Dory1.0-4B (60149), Nemotron Parse 3.5 (61651), MiniCPM-OCR 1.0 (63095), Domyn Edge (62544) and the ColUltraVBERT visual document retriever (62237).
 PyTorch 2.14: vLLM is upgraded to PyTorch 2.14.0, with torchvision 0.