MLX Swift: Enabling On-Device Large Language Models on Apple Silicon
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. We characterize the layered architecture of the MLX ecosystem, contrast its design philosophy with that of Apple’s Foundation Models API, and present a reference implementation demonstrating the complete inference lifecycle: model acquisition, session initialization, and autoregressive text generation. The discussion is grounded in the computational properties of unified memory and their implications for on-device inference efficiency. ...