vLLM
Easy, fast, and cheap LLM serving for everyone
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
- 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
- Seamless integration with popular HuggingFace models
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
-
Q:What is vLLM?
A:vLLM is a library for LLM inference and serving. -
Q:What are the key features of vLLM?
A:Key features include state-of-the-art serving throughput, efficient memory management, and seamless integration with HuggingFace models. -
Q:Does vLLM support NVIDIA GPUs?
A:Yes, vLLM supports NVIDIA GPUs.
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