vLLM

About

vLLM is an open-source library designed for fast and easy-to-use inference and serving of Large Language Models (LLMs). Developed initially at UC Berkeley, it has become a community-driven project known for its state-of-the-art serving throughput, achieved through innovative techniques like PagedAttention for efficient memory management, continuous batching, and optimized CUDA/HIP graph execution. It offers broad compatibility with popular Hugging Face models, various quantization methods (GPTQ, AWQ, FP8), and supports distributed inference through tensor and pipeline parallelism across a wide range of hardware, including NVIDIA, AMD, Intel, TPU, and AWS Neuron. vLLM also provides an OpenAI-compatible API server and features like speculative decoding, prefix caching, and multi-LoRA support, making LLM serving accessible, fast, and cost-effective.

Key Features

• Provides broad hardware compatibility across NVIDIA, AMD, Intel GPUs, TPUs, and AWS Neuron, with support for tensor and pipeline parallelism.
• State-of-the-art serving throughput with efficient memory management via PagedAttention.
• 49,224 GitHub stars
• Includes optimized CUDA kernels, integration with FlashAttention/FlashInfer, and various quantization methods (GPTQ, AWQ, INT4, INT8, FP8).
• Offers seamless integration with Hugging Face models, including Transformer-like, Mixture-of-Expert, Embedding, and Multi-modal LLMs.
• Supports continuous batching, speculative decoding, and chunked prefill for enhanced performance.

Use Cases

• Building and scaling production-grade LLM applications requiring high throughput and low latency.
• Deploying and serving Large Language Models (LLMs) with high performance and cost efficiency.
• Facilitating distributed LLM inference across diverse hardware accelerators and cloud environments.