Llama-3 on iPhone: A Guide to Private, On-Device AI Health Pre-Diagnosis

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A developer tutorial published on DEV Community outlines how to build a privacy-focused health pre-diagnosis system that runs entirely on an iPhone, with no data sent to external servers. The project uses Apple's MLX Swift framework alongside a 4-bit quantized version of Meta's Llama-3-8B model, reducing the model size from roughly 15GB to around 5GB to fit within mobile memory constraints. Apple's MLX Swift is designed for Apple Silicon's unified memory architecture, allowing the CPU and GPU to share model weights efficiently and enabling on-device large language model inference. Users input symptoms into a SwiftUI interface, and the app generates a pre-diagnosis report locally, ensuring complete data sovereignty. The setup requires Xcode 15.4 or later and a device equipped with at least an A17 Pro or M-series chip for optimal performance.

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