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

.. figure:: ./assets/logos/vllm-logo-text-light.png
  :width: 60%
  :align: center
  :alt: vLLM
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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
* Fast model execution with CUDA/HIP graph
* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_, FP8 KV Cache
* 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
* (Experimental) Prefix caching support
* (Experimental) Multi-lora support

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/neuron-installation
   getting_started/cpu-installation
   getting_started/quickstart
   getting_started/examples/examples_index

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

   serving/openai_compatible_server
   serving/deploying_with_docker
   serving/distributed_serving
   serving/metrics
   serving/env_vars
   serving/usage_stats
   serving/integrations

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

   models/supported_models
   models/adding_model
   models/engine_args
   models/lora
   models/performance

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

   quantization/auto_awq
   quantization/fp8_e5m2_kvcache
   quantization/fp8_e4m3_kvcache

.. toctree::
   :maxdepth: 2
   :caption: Developer Documentation

   dev/sampling_params
   dev/engine/engine_index
   dev/kernel/paged_attention
   dev/dockerfile/dockerfile

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
