Sharing your health data with a cloud provider can feel like handing over the keys to your most private vault. Whether it's a persistent cough or a weird rash, the moment you hit "send" on a GPT-4 prompt, that data lives on a server somewhere. But what if your phone could think for itself?
In this guide, we’re building a privacy-first health pre-diagnosis system using Local-first Health principles. By leveraging Edge AI and MLX Swift, we will deploy a quantized Llama-3-8B model directly on your iPhone. This allows for high-performance, on-device LLM inference that works without an internet connection, ensuring 100% data sovereignty.
If you're looking for more production-ready patterns for edge deployment or advanced quantization techniques, the team over at WellAlly Tech Blog has some incredible deep dives on making AI both accessible and secure.
Apple's MLX Swift is a game-changer for the iOS ecosystem. Unlike traditional wrappers, it’s designed specifically for Apple Silicon’s unified memory architecture. This means the CPU and GPU can share the model weights without redundant copying, making it possible to run an 8B parameter model on a modern iPhone or iPad.
Data Flow & Logic
