Introduction
vLLM is a fast, easy-to-use library for LLM inference and serving. It emphasizes high throughput and memory efficiency through techniques such as PagedAttention, continuous batching, optimized CUDA kernels, and multiple quantization options. vLLM integrates with Hugging Face models and provides an OpenAI-compatible API server for production deployment.
Key Features
- High-throughput serving with continuous batching and optimized execution.
- Memory-efficient attention management (PagedAttention) and prefix caching.
- Support for quantization (GPTQ, AWQ, AutoRound, INT4/INT8/FP8) and speculative decoding.
- Seamless integration with Hugging Face models and an OpenAI-compatible API.
- Cross-hardware support (NVIDIA, AMD, Intel, TPU, and plugins).
Use Cases
- Production LLM serving with high QPS and low latency requirements.
- Research and benchmarking for new inference techniques and kernels.
- Edge or cloud deployments that benefit from quantized model execution.
- Building OpenAI-compatible endpoints, streaming responses, or multi-tenant inference services.
Technical Highlights
- PagedAttention for efficient KV memory management.
- CUDA/HIP graph optimizations and specialized kernels (FlashAttention/FlashInfer).
- Continuous batching and chunked prefill for throughput improvements.
- Multi-LoRA support and compatibility with MoE models and multimodal LLMs.