Running GUI agents fully on-device changes latency, privacy, failure handling and memory planning

Most agent tutorials assume a cloud API is always available, but running the same agent entirely on-device reshapes the architecture in several ways. Latency splits into prefill and decode phases, so a quantized 4B model on an M4 Pro needs roughly four seconds before a first action token appears for a 2,000-token step. Privacy shifts from a vendor contract to a structural guarantee, since screen content never leaves the machine, which opens up work involving credentials or patient records. Failures become local memory or model-drift problems that are more debuggable but must be owned by the developer. Memory budgeting replaces rate-limit monitoring, with a 4B model peaking near 4.3GB, and a GUI-specialized 4B model reportedly ranks first on OSWorld at 58.2%. The article points to the open-source Mano-P project as a reference implementation for Apple Silicon.