On-Device Memory Scheduling

On-device memory scheduling is the systems problem 康宏文 Henry emphasizes in 为什么硅谷开始重新定义「AI 记忆」｜ S10E20. A local AI memory system cannot assume a dedicated cloud server. It must share CPU, GPU, NPU, memory, storage, and thermal budget with the user’s active applications.

The source treats this as a major reason local memory is harder than a simple local-model demo. The system needs to detect device configuration, foreground app state, available compute, current user experience, model/task priority, and whether work should happen locally, pause, or fall back to cloud assistance.

Key Claims

• Local memory products must schedule perception, indexing, retrieval, summarization, and agent tasks without making the user’s computer feel slow or unstable.
• Device heterogeneity makes edge AI harder than server-side AI: different Macs, Windows PCs, chips, memory sizes, drivers, and workloads create different constraints.
• Model optimization, operator-level tuning, and chip-specific adaptation can become product requirements when local AI is not yet mature.
• Edge-Cloud AI Boundary decisions are dynamic: local first does not mean local only when the device lacks enough compute for a task.

Connections

• Clipto AI and 康宏文 Henry — product and speaker case.
• On-Device AI and Edge-Cloud AI Boundary — broader device/cloud systems frame.
• Local-First Memory Layer and Multimodal Personal Memory — memory architecture that creates the scheduling need.
• AI Inference Cost Structure — cloud cost pressure that can make local scheduling valuable.
• Agent Permission Boundaries — adjacent issue because local memory systems may have broad file and device access.