Unlock the full power of Apple Silicon with the definitive guide to MLX Swift and Local LLMs.
The future of Artificial Intelligence is local. In Volume 6 of the Swift & AI Masterclass, author Edgar Milvus takes you deep into the architecture of Apple's MLX framework, a high-performance array library designed for the "Metal-to-Model" experience. This isn't just about calling APIs; it's about building custom inference engines and fine-tuning models directly on your Mac, iPhone, and iPad.
What’s inside this volume:
- Unified Memory Mastery: Learn to exploit zero-copy data sharing between CPU and GPU for lightning-fast tensor operations.
- Local LLM Deployment: Step-by-step guides on porting HuggingFace weights and running models like Llama and Mistral natively in Swift.
- Parameter-Efficient Fine-Tuning (LoRA): Teach your models new tricks using user-specific data without the cost of full retraining.
- Quantization & Performance: Master 4-bit and 8-bit quantization to run multi-billion parameter models on mobile devices.
- Streaming & Agentic Loops: Build responsive SwiftUI chat interfaces and autonomous agents that can call Swift functions as tools.
Bridging the gap between Python-based research and Swift-based production, this book provides the theoretical foundations and the production-ready code needed to build the next generation of privacy-centric, offline-first AI applications. Whether you are an experienced iOS developer or a Machine Learning engineer, this masterclass is your roadmap to AI excellence on Apple platforms.
Note: This book requires a Mac with Apple Silicon for the code examples.
Table of contents
Chapter 1: Intro to MLX — Apple’s Array Framework for Swift
Chapter 2: MLX Swift vs. Core ML — When to Use Which
Chapter 3: Unified Memory Architecture and Tensor Operations
Chapter 4: Building Neural Networks with MLX NN in Swift
Chapter 5: Optimization Techniques with MLX Optimizers
Chapter 6: Porting Weights — Converting HuggingFace Models to MLX
Chapter 7: Implementing Transformer Architectures in MLX Swift
Chapter 8: Quantization (4-bit/8-bit) for On-Device LLMs
Chapter 9: Streaming Token Inference with MLX Swift
Chapter 10: Performance Profiling MLX vs. llama.cpp
Chapter 11: Local Fine-Tuning with LoRA and QLoRA in Swift
Chapter 12: Memory Management for Large Models on Mac
Chapter 13: Building Agentic Loops with MLX-powered LLMs
Chapter 14: Tool Calling and Function Injection with MLX
Chapter 15: Deploying MLX Swift to macOS and iOS (The Future)
Chapter 16: Designing the MLX-based Model Service Actor
Chapter 17: Real-time UI with SwiftUI and Token Streaming
Chapter 18: Implementing Local Memory with MLX Embeddings
Chapter 19: Hardware Monitoring (GPU/NPU usage) in-app
Chapter 20: Optimization, Sandboxing, and Distribution
If printed, this ebook would span over 600 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.
