On-device frameworks (such as Apple's Core ML and Create ML) package models directly into the app and perform inference locally on the device; the advantages are low latency, offline availability, and data never leaving the device, while the drawbacks are that model size and compute power are limited by phone hardware, usually requiring quantization and compression beforehand.