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.