A high-throughput and memory-efficient inference and serving engine for LLMs

vllm

这是一个高效易用的大型语言模型推理引擎，专为解决推理速度慢、资源利用率低等问题而设计。它基于 PyTorch 和 CUDA，并结合内存优化算法（PagedAttention）、计算图优化和模型并行技术，大幅降低 GPU 内存占用，并充分利用多 GPU 资源提升推理性能。同时，vLLM 与 HF 模型无缝兼容。支持在 GPU、CPU、TPU 等多种硬件平台上高效运行，适用于实时问答、文本生成和推荐系统等场景。

This is a highly efficient and user-friendly large language model inference engine, specifically designed to address issues such as slow inference speeds and low resource utilization. It is based on PyTorch and CUDA, and incorporates memory optimization algorithms (PagedAttention), computational graph optimization, and model parallelization techniques to significantly reduce GPU memory usage and fully leverage multi-GPU resources to enhance inference performance. At the same time, vLLM is seamlessly compatible with HF models. It supports efficient operation on a variety of hardware platforms such as GPUs, CPUs, and TPUs, suitable for real-time question answering, text generation, and recommendation systems.

vllm-project/vllm

vllm-project/vllm

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