Local-First Health: Running Llama-3 on iOS with MLX Swift for 100% Private Diagnostics A developer built a privacy-first health pre-diagnosis system using MLX Swift and a quantized Llama-3-8B model on iOS. The system runs entirely on-device, ensuring 100% data sovereignty by processing symptoms locally without an internet connection. The approach leverages Apple Silicon's unified memory architecture for efficient inference. 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 https://www.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. Here is how the symptom pre-diagnosis data flows through the system: php graph TD A User Inputs Symptoms -- B{Local Swift App} B -- C MLX Swift Runner C -- D Quantized Llama-3-8B Weights D -- E Unified Memory / GPU Acceleration E -- F Privacy-Safe Diagnosis Report F -- B B -- G Display to User style D fill: f96,stroke: 333,stroke-width:2px style E fill: 00ff,stroke: fff,stroke-width:2px To follow along, you’ll need: Running a full 16-bit Llama-3-8B is too heavy for mobile RAM. We use 4-bit quantization to shrink the model from ~15GB to ~5GB. You can use the mlx-lm Python tool to convert the weights before importing them into your Xcode project: Convert and quantize Llama-3-8B-Instruct python -m mlx lm.convert --hf-path meta-llama/Meta-Llama-3-8B-Instruct -q --q-bits 4 In your Swift project, you need a manager to handle the model loading and token generation. We'll utilize the MLXLLM library to interface with our local weights. python import Foundation import MLX import MLXLLM @Observable class HealthAIEngine { var modelConfiguration = ModelConfiguration.llama3 8B 4bit private var model: LLMModel? private var tokenizer: Tokenizer? func loadModel async throws { // Load the model and tokenizer from the app bundle let model, tokenizer = try await LLMModel.load configuration: modelConfiguration self.model = model self.tokenizer = tokenizer print "✅ Local Llama-3 Loaded Successfully" } func generateDiagnosis symptoms: String async - AsyncThrowingStream