On-Device AI on Apple Silicon - What It Means for Desktop Agents

On-Device AI on Apple Silicon Apple Silicon changed what is possible for local AI. The unified memory architecture means ML models can run on the GPU without copying data between CPU and GPU memory. For a desktop agent that needs to process screen content in real-time, this matters a lot. What Runs Locally Now On an M1 with 16GB of RAM, you can comfortably run: WhisperKit for voice transcription - fast enough for real-time push-to-talk Ollama with 7-13B parameter models for action planning - usable latency for simple tasks Vision models for screen understanding - when accessibility APIs are not enough On an M4 Pro with 48GB, the picture gets much better: 32B parameter models run at interactive speeds Multiple models simultaneously - transcription and planning can run in parallel without contention Overnight batch processing - the agent can process files, organize documents, and handle backlog tasks while you sleep The Latency Question Cloud APIs add 500ms-2s per request. For a desktop