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    <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/logos/vllm-logo-text-dark.png">
    <img alt="vLLM" src="./docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
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<h3 align="center">
Easy, fast, and cheap LLM serving for everyone
</h3>

<p align="center">
| <a href="https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/"><b>Documentation</b></a> | <a href=""><b>Blog</b></a> |

</p>

---

*Latest News* 🔥

- [2023/06] We officially released vLLM! vLLM has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid April. Check out our [blog 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 with **PagedAttention**
- Dynamic batching of incoming requests
- Optimized CUDA kernels

vLLM is flexible and easy to use with:

- Seamless integration with popular HuggingFace models
- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
- Tensor parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server

Install vLLM with pip or [from source](https://llm-serving-cacheflow.readthedocs-hosted.com/en/latest/getting_started/installation.html#build-from-source):

```bash
pip install vllm
```

## Getting Started

Visit our [documentation](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/) to get started.
- [Installation](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/getting_started/installation.html)
- [Quickstart](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/getting_started/quickstart.html)
- [Supported Models](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/models/supported_models.html)

## Performance

vLLM outperforms HuggingFace Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
For details, check out our [blog post]().

<p align="center">
  <picture>
  <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a10g_n1_dark.png">
  <img src="./docs/source/assets/figures/perf_a10g_n1_light.png" width="45%">
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  <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n1_dark.png">
  <img src="./docs/source/assets/figures/perf_a100_n1_light.png" width="45%">
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  <br>
  <em> Serving throughput when each request asks for 1 output completion. </em>
</p>

<p align="center">
  <picture>
  <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a10g_n3_dark.png">
  <img src="./docs/source/assets/figures/perf_a10g_n3_light.png" width="45%">
  </picture>
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  <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n3_dark.png">
  <img src="./docs/source/assets/figures/perf_a100_n3_light.png" width="45%">
  </picture>  <br>
  <em> Serving throughput when each request asks for 3 output completions. </em>
</p>

## Contributing

We welcome and value any contributions and collaborations.
Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
