index.rst

Welcome to vLLM!
================

.. figure:: ./assets/logos/vllm-logo-text-light.png
  :width: 60%
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   <strong>Easy, fast, and cheap LLM serving for everyone
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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**
* Continuous batching of incoming requests
* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_
* 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
* Support NVIDIA GPUs and AMD GPUs.

For more information, check out the following:

* `vLLM announcing blog post <https://vllm.ai>`_ (intro to PagedAttention)
* `vLLM paper <https://arxiv.org/abs/2309.06180>`_ (SOSP 2023)
* `How continuous batching enables 23x throughput in LLM inference while reducing p50 latency <https://www.anyscale.com/blog/continuous-batching-llm-inference>`_ by Cade Daniel et al.



Documentation
-------------

.. toctree::
   :maxdepth: 1
   :caption: Getting Started

   getting_started/installation
   getting_started/amd-installation
   getting_started/quickstart

.. toctree::
   :maxdepth: 1
   :caption: Serving

   serving/distributed_serving
   serving/run_on_sky
   serving/deploying_with_triton
   serving/deploying_with_docker
   serving/serving_with_langchain
   serving/metrics

.. toctree::
   :maxdepth: 1
   :caption: Models

   models/supported_models
   models/adding_model
   models/engine_args

.. toctree::
   :maxdepth: 1
   :caption: Quantization

   quantization/auto_awq