vllm

vLLM is a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). It enables easy, fast, and cost-efficient LLM serving for everyone, with a drop-in OpenAI-compatible API for instant integration. vLLM maximizes throughput using PagedAttention, advanced scheduling, and continuous batching to ensure peak GPU utilization. It supports a wide range of open-source models (e.g., DeepSeek, Llama, Mistral, Qwen) across diverse hardware platforms including NVIDIA CUDA GPUs, AMD ROCm, Intel Gaudi, Apple Silicon, AWS Neuron, Google Cloud TPU, and more. The engine is designed to slash inference costs by maximizing hardware efficiency, making high-performance LLMs affordable and accessible. vLLM is a community project sponsored by organizations such as a16z, Sequoia Capital, and supported by compute resources from Alibaba Cloud, AMD, AWS, Google Cloud, and others.