index.md

# Welcome to vLLM

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: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.

Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evloved into a community-driven project with contributions from both academia and industry.

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
- Fast model execution with CUDA/HIP graph
- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8
- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
- Speculative decoding
- Chunked prefill

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 and pipeline parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, Gaudi® accelerators and GPUs, PowerPC CPUs, TPU, and AWS Trainium and Inferentia Accelerators.
- Prefix caching support
- 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.
- [vLLM Meetups](#meetups)

## Documentation

% How to start using vLLM?

:::{toctree}
:caption: Getting Started
:maxdepth: 1

getting_started/installation/index
getting_started/quickstart
getting_started/examples/examples_index
getting_started/troubleshooting
getting_started/faq
:::

% What does vLLM support?

:::{toctree}
:caption: Models
:maxdepth: 1

models/generative_models
models/pooling_models
models/supported_models
models/extensions/index
:::

% Additional capabilities

:::{toctree}
:caption: Features
:maxdepth: 1

features/quantization/index
features/lora
features/tool_calling
features/reasoning_outputs
features/structured_outputs
features/automatic_prefix_caching
features/disagg_prefill
features/spec_decode
features/compatibility_matrix
:::

% Details about running vLLM

:::{toctree}
:caption: Inference and Serving
:maxdepth: 1

serving/offline_inference
serving/openai_compatible_server
serving/multimodal_inputs
serving/distributed_serving
serving/metrics
serving/engine_args
serving/env_vars
serving/usage_stats
serving/integrations/index
:::

% Scaling up vLLM for production

:::{toctree}
:caption: Deployment
:maxdepth: 1

deployment/docker
deployment/k8s
deployment/nginx
deployment/frameworks/index
deployment/integrations/index
:::

% Making the most out of vLLM

:::{toctree}
:caption: Performance
:maxdepth: 1

performance/optimization
performance/benchmarks
:::

% Explanation of vLLM internals

:::{toctree}
:caption: Design Documents
:maxdepth: 2

design/arch_overview
design/huggingface_integration
design/plugin_system
design/kernel/paged_attention
design/mm_processing
design/automatic_prefix_caching
design/multiprocessing
:::

% How to contribute to the vLLM project

:::{toctree}
:caption: Developer Guide
:maxdepth: 2

contributing/overview
contributing/profiling/profiling_index
contributing/dockerfile/dockerfile
contributing/model/index
contributing/vulnerability_management
:::

% Technical API specifications

:::{toctree}
:caption: API Reference
:maxdepth: 2

api/offline_inference/index
api/engine/index
api/inference_params
api/multimodal/index
api/model/index
:::

% Latest news and acknowledgements

:::{toctree}
:caption: Community
:maxdepth: 1

community/blog
community/meetups
community/sponsors
:::

## Indices and tables

- {ref}`genindex`
- {ref}`modindex`