Tepi AI

Identify what must happen locally and what can be deferred or sent to a server. An offline feature needs a useful failure mode when connectivity disappears; a local model that still depends on remote retrieval may not be fully offline. Measure memory, compute, battery use, heat, and sustained performance on the actual device class. A short benchmark can miss thermal throttling or competition with other applications. Model size alone does not account for working memory and concurrent tasks. Compression, quantization, or a smaller architecture may help fit the workload, but evaluate the task after each change. Check difficult inputs and conditions from the intended environment, such as poor lighting, noisy audio, or low battery. Plan updates and data handling. Local processing can reduce some data transfers, but logs, synchronization, and connected features still need privacy controls. Keep model versions identifiable and support a safe update or rollback path across devices that may reconnect infrequently.