Edge Tiny-LoRA Systems Synthesis
The browser and tiny-model reports show why modular AI will not remain confined to central server stacks. Small quantized models, WebAssembly, WebGPU, IndexedDB caching, and LoRA hot-swapping make local composition increasingly plausible.
Tiny local models still need full composition evidence.
Privacy and low latency improve when inference moves to the browser, but local adapters, caches, service workers, and route decisions become part of the safety boundary.
Benefits
Edge systems can improve privacy, latency, offline operation, cost, and user control. They also support narrow specialist modules that are easier to reason about than a single large generalized service.
Risks
The same properties create local supply-chain and composition hazards. A cached adapter may outlive its evaluation. A browser router may select a different skill stack than the one tested. A service worker may update modules faster than review can repeat.
Controls
The synthesis recommends signed manifests, allowed lists, immutable content hashes, local cache reset, explicit module compatibility, and no silent unknown adapter loading.