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edge-cloud-placement

bojieli/ai-infra-book/skills/edge-cloud-placement/SKILL.md

比较模型或任务放在端侧、边缘或云端时的交互延迟、网络传输、能耗与恢复。用于多模态和实时 Agent 部署。

Skill6.1k starsChanged 27 days ago

What's in it

  1. 端边云部署选择
---
name: edge-cloud-placement
description: 比较模型或任务放在端侧、边缘或云端时的交互延迟、网络传输、能耗与恢复。用于多模态和实时 Agent 部署。
---

# 端边云部署选择

适用:同一交互可在本地、边缘或云端执行,需比较完整体验。依据 `manuscripts/12-端边云协同.md` 第 12.1—12.5 节。

1. 固定质量要求与计时边界,列出采集、编码、上传、排队、推理、下行和展示的字节量与耗时。指出哪些可流式重叠,哪些有前后依赖。
2. 端侧先检查模型和上下文能否放下,再按内存带宽、算力、功率与电池估算可持续速度。跨设备切分时计算传输的是原始数据、特征还是中间激活,以及需要同步的次数。
3. 用实际 RTT、上/下行带宽、丢包和无线波动估算等待;不要用一次理想测速代替高分位交互延迟。连接预热、状态迁移和缓存复用应按预计调用次数摊销。
4. 沿完整关键路径比较方案,同时报告失败后恢复、重做量、按时完成概率和成本。局部模型加速只有落在关键路径上才改善用户体验。

输出端/边/云各方案的质量、容量、延迟分解、能耗/成本与恢复假设。缺少网络或设备实测时给出使选择翻转的阈值,而非断言某一位置始终更好。

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