vllm/docs/source/index.md

Welcome to 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, AWQ, 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 (intro to PagedAttention)
• vLLM paper (SOSP 2023)
• How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al.
• {ref}vLLM Meetups <meetups>.

Documentation

:caption: Getting Started
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getting_started/installation
getting_started/amd-installation
getting_started/openvino-installation
getting_started/cpu-installation
getting_started/gaudi-installation
getting_started/arm-installation
getting_started/neuron-installation
getting_started/tpu-installation
getting_started/xpu-installation
getting_started/quickstart
getting_started/debugging
getting_started/examples/examples_index

:caption: Serving
:maxdepth: 1

serving/openai_compatible_server
serving/deploying_with_docker
serving/deploying_with_k8s
serving/deploying_with_helm
serving/deploying_with_nginx
serving/distributed_serving
serving/metrics
serving/integrations
serving/tensorizer
serving/runai_model_streamer

:caption: Models
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models/supported_models
models/generative_models
models/pooling_models
models/adding_model
models/enabling_multimodal_inputs

:caption: Usage
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usage/lora
usage/multimodal_inputs
usage/tool_calling
usage/structured_outputs
usage/spec_decode
usage/compatibility_matrix
usage/performance
usage/faq
usage/engine_args
usage/env_vars
usage/usage_stats
usage/disagg_prefill

:caption: Quantization
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quantization/supported_hardware
quantization/auto_awq
quantization/bnb
quantization/gguf
quantization/int8
quantization/fp8
quantization/fp8_e5m2_kvcache
quantization/fp8_e4m3_kvcache

:caption: Automatic Prefix Caching
:maxdepth: 1

automatic_prefix_caching/apc
automatic_prefix_caching/details

:caption: Performance
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performance/benchmarks

% Community: User community resources

:caption: Community
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community/meetups
community/sponsors

% API Documentation: API reference aimed at vllm library usage

:caption: API Documentation
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dev/sampling_params
dev/pooling_params
dev/offline_inference/offline_index
dev/engine/engine_index

% Design: docs about vLLM internals

:caption: Design
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design/arch_overview
design/huggingface_integration
design/plugin_system
design/input_processing/model_inputs_index
design/kernel/paged_attention
design/multimodal/multimodal_index
design/multiprocessing

% For Developers: contributing to the vLLM project

:caption: For Developers
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contributing/overview
contributing/profiling/profiling_index
contributing/dockerfile/dockerfile

Indices and tables

• {ref}genindex
• {ref}modindex