vLLM

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.