vLLM: высокопроизводительный serving с OpenAI-совместимым API
vLLM — open-source движок для serving LLM с высокой пропускной способностью и низкой стоимостью за счет PagedAttention и continuous batching, предоставляет OpenAI-совместимый API.
vLLM — open-source движок для serving LLM с высокой пропускной способностью и низкой стоимостью за счет PagedAttention и continuous batching, предоставляет OpenAI-совместимый API.
vLLM можно использовать как основу для оркестрации моделей: развернуть его как высокопроизводительный inference-сервер, обеспечивающий OpenAI-совместимый API, что позволит легко подключать различные LLM к вашим агентам и автоматизациям. Благодаря поддержке множества моделей и аппаратных платформ, vLLM дает гибкость в выборе моделей под задачи, а оптимизации (PagedAttention, continuous batching) снижают затраты на инфраструктуру. Это напрямую применимо для создания масштабируемой и экономичной инфраструктуры для AI-оператора.
расшифровка ролика ↓
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<h3 align="center"> Easy, fast, and cheap LLM serving for everyone </h3>
<p align="center"> | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | </p>
🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more. For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
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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](https://sky.cs.berkeley.edu) 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**](https://blog.vllm.ai/2023/06/20/vllm.html) - 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](https://docs.vllm.ai/en/latest/features/quantization/index.html) - 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, 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](https://docs.vllm.ai/en/latest/models/supported_models.html).
## Getting Started
Install vLLM with [`uv`](https://docs.astral.sh/uv/) (recommended) or `pip`:
```bash uv pip install vllm ```
Or [build from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source) for development.
Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html) - [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html) - [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
## Contributing
We welcome and value any contributions and collaborations. Please check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.
## Citation
If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
```bibtex @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
<!-- --8<-- [start:contact-us] --> - For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai) - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai) - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature - For collaborations and partnerships, please contact us at [collaboration@vllm.ai](mailto:collaboration@vllm.ai) <!-- --8<-- [end:contact-us] -->
## Media Kit
- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit)