vLLM: High-Throughput and Memory-Efficient Inference Engine for LLMs
vLLM is a fast and easy-to-use library designed for large language model (LLM) inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, it has evolved into a community-driven project with contributions from both academia and industry.
Key Features:
- High Throughput: State-of-the-art serving throughput with efficient management of attention key and value memory using PagedAttention.
- Flexible Integration: Seamlessly supports popular open-source models from Hugging Face.
- Performance Benchmarking: Includes performance benchmarks comparing vLLM against other LLM serving engines.
- Easy Installation: Install via pip or from source with simple commands.
- Community Driven: Welcomes contributions and collaborations from users and developers.
Benefits:
- Cost-Effective: Designed to be easy, fast, and cheap for LLM serving.
- Optimized Execution: Fast model execution with CUDA/HIP graph and optimized CUDA kernels.
- Support for Multiple Architectures: Compatible with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, and more.
Highlights:
- Continuous batching of incoming requests.
- Support for various decoding algorithms, including parallel sampling and beam search.
- OpenAI-compatible API server.
- Multi-modal LLM support and prefix caching capabilities.
For more information, visit the vLLM GitHub repository.
