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本期将手把手教你如何使用 vLLM 部署 Qwen3 模型,并结合 Open-WebUI 搭建一个本地可用、交互友好、功能强大的 AI 对话界面。 This generation delivers comprehensive upgrades acr 本教程详细介绍了在Linux系统 (VSCode+Python 3.10.18)下部署Qwen3-8B模型的完整流程。 首先指导用户完成CUDA 12.x和PyTorch环境配置,然后通过ModelScope下载模型文件。 vLLM部署大模型服务 部署环境 操作系统:Ubuntu 20.04.4 LTS Cuda Version: 12.8, Cuda compilation tools, release 12.2, V12.2.91 显卡:A100 40G × 2 Miniconda3 vllm==0.8.
安装 vLLM 1)创建 conda 环境 # 创建 conda 虚拟环境,环境名称为 vllm,python 的版本为 3.10 conda create -n vllm python = 3.10 2)切换 vllm 环境 conda activate vllm 3)安装 vllm pip install -U vllm \ --pre \ --extra-index-url https://wheels.vllm.ai/nightly 本教程手把手教你用vLLM在消费级显卡上部署Qwen3-Coder-30B-A3B-Instruct-FP8,并配置Continue实现Chatbot、Agent与FIM代码补全三合一。 本文将手把手教你使用高性能推理引擎vLLM部署Qwen大模型,并提供Python和Java两种语言的实现代码,帮助不同技术背景的开发者快速上手。 本文主要讲述如何使用 vLLM 部署 千问 3 大语言模型。 本文使用的是 Qwen3-0.6B 一个参数较小的模型,主要是为了体验整个部署过程。
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