On-Device AI

In Part 1 of this series, we set up the MLX ecosystem and ran a language model locally on Apple Silicon. If you haven’t read it yet, it’s worth starting there. This article tackles the question that naturally follows: how do you fit a multi-billion parameter model into a device with 8 GB of RAM? The answer is quantization — and understanding it will change how you think about on-device AI. ...

Abstract The proliferation of large-scale neural language models has, until recently, been contingent upon access to remote computational infrastructure. The architectural characteristics of Apple Silicon — most notably its unified memory subsystem — present a substantive departure from this dependency. This article examines MLX Swift, a native Swift binding to Apple’s MLX machine learning framework, as a mechanism for deploying quantized Large Language Models (LLMs) directly on consumer Apple hardware. ...