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- GitHub - vllm-project vllm: A high-throughput and memory-efficient . . .
vLLM is a fast and easy-to-use library for LLM inference and serving Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry
- Welcome to vLLM — vLLM
vLLM is a fast and easy-to-use library for LLM inference and serving Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry
- vLLM - vLLM 文档
vLLM 是一个用于 LLM 推理和服务的快速易用库。 vLLM 最初由加州大学伯克利分校的 Sky Computing Lab 开发，现已发展成为一个由学术界和工业界共同贡献的社区驱动项目。 优化的 CUDA 内核，包括与 FlashAttention 和 FlashInfer 的集成。 支持 NVIDIA GPU、AMD CPU 和 GPU、Intel CPU、Gaudi® 加速器和 GPU、IBM Power CPU、TPU 以及 AWS Trainium 和 Inferentia 加速器。
- vLLM – PyTorch
vLLM is an open source library for fast, easy-to-use LLM inference and serving It optimizes hundreds of language models across diverse data-center hardware—NVIDIA and AMD GPUs, Google TPUs, AWS Trainium, Intel CPUs—using innovations such as PagedAttention, chunked prefill, multi-LoRA and automatic prefix caching
- 快速开始 | vLLM 中文站
本指南将帮助您快速开始使用 vLLM 进行以下操作： 如果您使用的是 NVIDIA GPU，可以直接使用 pip 安装 vLLM。 推荐使用 uv （一个非常快速的 Python 环境管理器）来创建和管理 Python 环境。 请按照 文档 安装 uv。 安装完成后，您可以使用以下命令创建一个新的 Python 环境并安装 vLLM： 另一种便捷的方式是使用 uv run 命令的 --with [dependency] 选项，它允许您在不创建环境的情况下运行诸如 vllm serve 的命令： 您也可以使用 conda 来创建和管理 Python 环境。 注意： 对于非 CUDA 平台，请参考 安装 获取安装 vLLM 的具体说明。
- How to run vLLM on CPUs with OpenShift for GPU-free inference
vLLM is a production-grade inference engine, primarily optimized for GPUs and other hardware accelerators like TPUs However, it does support basic inference on CPUs as well That said, no official pre-built container images exist for CPU-only use cases To deploy vLLM on my OpenShift cluster, I needed to build and publish a custom image
- Meet vLLM: For faster, more efficient LLM inference and serving
With the need for LLM serving to be affordable and efficient, vLLM arose from a research paper called, “Efficient Memory Management for Large Language Model Serving with Paged Attention," from September of 2023, which aimed to solve some of these issues through eliminating memory fragmentation, optimizing batch execution and distributing
- vllm·PyPI
vLLM is a fast and easy-to-use library for LLM inference and serving Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry
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