为不同客户运营10+微调模型：运营指南
面向管理10+跨多客户微调模型的AI机构运营指南——涵盖模型组织、资源分配、监控、更新和无混乱扩展。
三个客户时，你可以记在脑子里。五个时，电子表格够用。十个时，某些东西会崩溃——模型部署到错误的客户，更新覆盖了生产适配器，或者你不知道哪个GPU在运行什么。
模型组织系统
命名约定：{客户}-{任务}-{基础模型}-v{主版本}.{次版本}.{补丁}
例如：acme-support-llama3-v2.1.0
LoRA适配器库
核心：一个基础模型加载到VRAM服务多个LoRA适配器。不需要为不同客户单独的模型实例，只需共享基础设施上的独立适配器。
资源规划
- 单RTX 4090（24GB）： 1个基础模型 + 3-5个适配器
- 双RTX 4090： 2个基础模型 + 6-10个适配器
- A100 80GB： 2-3个小基础模型 + 10-15个适配器
监控要素
- 每模型延迟 — P50、P95、P99
- 准确率漂移 — 每周自动评估
- 使用量追踪 — 用于容量规划和客户计费
- 每客户成本分配
更新工作流
- 从不就地重训。A/B部署：10%流量 → 监控 → 逐步增加
- 回滚应少于60秒
常见扩展错误
- 每客户一个基础模型（浪费90%+ VRAM）
- 无版本控制
- 手动部署
- 忽视资源争用
- 不追踪成本
Ship AI that runs on your users' devices.
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相关阅读：multi-tenant deployment architecture、per-client LoRA adapters、reducing costs。
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.
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