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服务，硬件利用率显著提升。\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>内容\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>平台\u003C\u002Ftd>\n\u003Ctd>Linux 为主，支持 NVIDIA、AMD、Intel GPU 与 x86\u002FARM CPU\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>语言\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>许可\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>模型支持\u003C\u002Ftd>\n\u003Ctd>200+ Hugging Face 架构\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"主要功能\">主要功能\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>PagedAttention\u003C\u002Fstrong>：像管理操作系统内存页一样管理 KV Cache，显存利用率高\u003C\u002Fli>\n\u003Cli>\u003Cstrong>连续批处理\u003C\u002Fstrong>：请求随到随入批，配合分块预填充与前缀缓存\u003C\u002Fli>\n\u003Cli>\u003Cstrong>高性能内核\u003C\u002Fstrong>：内置 FlashAttention、FlashInfer 等优化注意力内核\u003C\u002Fli>\n\u003Cli>\u003Cstrong>量化支持\u003C\u002Fstrong>：FP8、INT8、GPTQ、AWQ、GGUF 等多种量化方案\u003C\u002Fli>\n\u003Cli>\u003Cstrong>OpenAI 兼容 API\u003C\u002Fstrong>：直接对接现有 OpenAI SDK 生态，另有 Anthropic 与 gRPC 接口\u003C\u002Fli>\n\u003Cli>\u003Cstrong>结构化输出\u003C\u002Fstrong>：用 xgrammar 等约束生成 JSON 等固定格式\u003C\u002Fli>\n\u003Cli>\u003Cstrong>分布式推理\u003C\u002Fstrong>：张量、流水线、数据、专家并行多种切分方式\u003C\u002Fli>\n\u003Cli>\u003Cstrong>多 LoRA\u003C\u002Fstrong>：一个服务实例同时挂载多个 LoRA 适配器\u003C\u002Fli>\n\u003Cli>\u003Cstrong>投机解码\u003C\u002Fstrong>：n-gram、EAGLE 等方案加速生成\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"适用人群\">适用人群\u003C\u002Fh2>\u003Cul>\n\u003Cli>自托管大模型 API 的后端工程师\u003C\u002Fli>\n\u003Cli>需要批量离线推理的数据团队\u003C\u002Fli>\n\u003Cli>把开源模型部署到生产环境的企业\u003C\u002Fli>\n\u003Cli>研究 LLM 服务性能的算法工程师\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"快速上手\">快速上手\u003C\u002Fh2>\u003Col>\n\u003Cli>安装（推荐 uv）：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">uv pip install vllm\nvllm download-kernels\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>启动 OpenAI 兼容服务：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">vllm serve <模型名>\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>把 OpenAI SDK 指向本地端口即可调用，详见官方快速开始文档\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"核心优势\">核心优势\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>吞吐高\u003C\u002Fstrong>：同等硬件下服务能力大幅提升，成本随之降低\u003C\u002Fli>\n\u003Cli>\u003Cstrong>生态兼容\u003C\u002Fstrong>：Hugging Face 模型即插即用，API 与 OpenAI 一致\u003C\u002Fli>\n\u003Cli>\u003Cstrong>硬件面广\u003C\u002Fstrong>：NVIDIA、AMD、Intel GPU 与 CPU、TPU 等多种后端\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"系统要求\">系统要求\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>要求\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>操作系统\u003C\u002Ftd>\n\u003Ctd>Linux 为主\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>硬件\u003C\u002Ftd>\n\u003Ctd>NVIDIA\u002FAMD\u002FIntel GPU、x86\u002FARM CPU、TPU 等\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>语言环境\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"注意事项\">注意事项\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>安装环境\u003C\u002Fstrong>：GPU 版需按本机 CUDA 环境选择对应 PyTorch wheel，从源码编译更复杂\u003C\u002Fli>\n\u003Cli>\u003Cstrong>内核预下载\u003C\u002Fstrong>：安装后执行 \u003Ccode>vllm download-kernels\u003C\u002Fcode> 可加快服务启动\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197965978]