如何将AI工作负载从云端迁移到本地：企业手册
分阶段、逐步指南，将AI工作负载从云迁移到本地基础设施。涵盖工作负载分类、基础设施规划、数据管道迁移和常见陷阱。
将AI工作负载从云端迁移到本地不是单个项目，而是一系列审慎的步骤。采用"大爆炸"方式的组织往往错过时间线、超出预算并中断生产系统。采用分阶段方法的组织迁移更快且运营风险更低。
六个阶段
阶段1：审计当前云AI工作负载和成本 — 生成完整的工作负载清单和成本归因模型。
阶段2：分类工作负载 — 按数据敏感度、利用率模式、延迟需求和成本轨迹评分。16-20分适合立即迁移。
阶段3：构建本地基础设施 — 按需求规格硬件。准备软件栈与硬件采购并行进行。
阶段4：先迁移数据准备 — 处理最敏感数据、成本最密集、生产依赖最少。
阶段5：迁移推理工作负载 — 蓝绿方式：5% → 25% → 50% → 75% → 100%流量逐步切换。
阶段6：评估训练工作负载位置 — 根据训练频率决定：一次性用云，每周或持续则本地。
常见陷阱
- 低估数据引力
- 未考虑运维人员配备
- 一次迁移所有
- 硬件到货才开始软件栈准备
- 将迁移当作一次性项目
时间线
从决策到首个生产工作负载上线：8-16周。
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