vLLM

vLLM

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. It offers state-of-the-art serving throughput, efficient management of attention key and value memory, continuous batching of incoming requests, fast model execution with CUDA/HIP graph, and various decoding algorithms. The tool is flexible with seamless integration with popular HuggingFace models, high-throughput serving, tensor parallelism support, and streaming outputs. It supports NVIDIA GPUs and AMD GPUs, Prefix caching, and Multi-lora. vLLM is designed to provide fast and efficient LLM serving for everyone.

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Features

Advantages

  • High throughput serving
  • Flexible and easy to use
  • Support for NVIDIA GPUs and AMD GPUs
  • Various decoding algorithms
  • Tensor parallelism support

Disadvantages

  • Experimental support for AMD GPUs
  • Prefix caching support is experimental
  • Limited documentation on some features

Frequently Asked Questions

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