vLLM Overview
vLLM is a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). It seamlessly integrates with popular models from hubs like Hugging Face and offers a simple Python-based API. At its core is PagedAttention, a novel attention algorithm that manages key-value caches with near-zero memory waste by treating GPU memory like an operating system's virtual memory. This innovation allows for significantly larger batch sizes and provides state-of-the-art serving throughput.
vLLM also implements continuous batching, highly optimized CUDA kernels, and distributed inference through tensor parallelism. Inference requests are processed dynamically in a continuous stream rather than in static batches, which maximizes GPU utilization and dramatically reduces latency for real-world workloads.
For more information about vLLM, including documentation and examples, see: