vLLM - A high-throughput and memory-efficient library for LLM inference and serving.

## Overview of vLLM vLLM is an open-source library designed for efficient inference and serving of large language models (LLMs). Its primary purpose is to enhance the throughput and memory efficiency of LLM services using the Paged Attention algorithm. It supports various hardware platforms and integrates seamlessly with popular models like Hugging Face. ## Development and Status of vLLM vLLM was initially developed by the UC Berkeley Sky Computing Lab. It is now a community-driven project, actively maintained by both academic and industrial contributors. ## Paged Attention Algorithm in vLLM The Paged Attention algorithm is a mechanism inspired by the paged memory management of operating systems. It divides the key-value (KV) cache of requests into fixed-size blocks and manages the mapping between logical and physical blocks through a block table. This reduces memory fragmentation and allows different requests to share blocks, significantly improving the efficiency and throughput of large model deployments. ## Key Characteristics of vLLM vLLM offers several key characteristics, including high performance through efficient memory management with Paged Attention, support for continuous batching of incoming requests, fast model execution via CUDA/HIP graphs, and compatibility with various quantization methods like GPTQ and AWQ. It also supports distributed inference, integrates with Hugging Face models, and is compatible with multiple hardware platforms including NVIDIA GPU, AMD CPU and GPU, Intel CPU and GPU, PowerPC CPU, TPU, and AWS Neuron. ## Installation and Usage of vLLM vLLM can be installed using the command `pip install vllm`. For detailed usage instructions, including how to initialize the vLLM engine, load models, and process user prompts, users can refer to the official documentation available at [vLLM Documentation](https://docs.vllm.ai/en/latest/). ## Advanced Features of vLLM vLLM supports advanced features such as speculative decoding, chunked prefill, streaming output, an OpenAI-compatible API server, prefix caching, and multi-lora support. These features enhance its processing capabilities and make it suitable for high-throughput and memory-efficient scenarios. ## Hardware Compatibility of vLLM vLLM is compatible with a wide range of hardware platforms, including NVIDIA GPU, AMD CPU and GPU, Intel CPU and GPU, PowerPC CPU, TPU, and AWS Neuron. ## Resources for vLLM Users can find more information about vLLM, including detailed documentation, performance benchmarks, and community contributions, on its official GitHub repository at [vLLM GitHub](https://github.com/vllm-project/vllm) and the official documentation at [vLLM Documentation](https://docs.vllm.ai/en/latest/). ### Citation sources: - [vLLM](https://github.com/vllm-project/vllm) - Official URL Updated: 2025-03-28