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portada Engineering AI on Apple Silicon. Unified Memory, Metal Compute, MLX, and Core ML for On-Device Intelligence
Type
Physical Book
Publisher
Author
Year
2026
Language
English
Pages
300
Format
Paperback
Dimensions
24.4x17x1.5 cm
ISBN13
9798259076129

Engineering AI on Apple Silicon. Unified Memory, Metal Compute, MLX, and Core ML for On-Device Intelligence

Albert V. Chitwood (Author) · Independently published · Paperback

Engineering AI on Apple Silicon. Unified Memory, Metal Compute, MLX, and Core ML for On-Device Intelligence - Albert V. Chitwood

New Book Imported to New Zealand
Delivery: 21 Oct - 28 Oct Shipping: 6 to 7 business days.
NZ$ 71.29
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Synopsis "Engineering AI on Apple Silicon. Unified Memory, Metal Compute, MLX, and Core ML for On-Device Intelligence"

Stop Paying for Cloud AI Compute. Master Apple Silicon and Build High-Performance, Privacy-First AI On-Device.
The future of AI is local. Relying on cloud APIs introduces latency, recurring costs, and severe data privacy risks. Apple's M-Series chips with their Unified Memory architecture and dedicated Neural Engines have fundamentally changed the hardware landscape, turning standard laptops into supercomputers capable of running massive models entirely on-device.
Engineering AI on Apple Silicon is the definitive, commercially focused blueprint for mastering this ecosystem. Whether you are prototyping with MLX, optimizing inference with Metal, or shipping production-ready binaries via Core ML, this book bridges the gap between raw hardware constraints and highly marketable, user-facing AI applications.
Inside, you will discover:
Unified Memory Mastery: Stop treating a Mac like a standard PC. Learn how shared address spaces eliminate PCIe bottlenecks to unlock unprecedented inference throughput.
The MLX to Core ML Pipeline: Master the complete lifecycle - train and prototype rapidly using Apple's MLX framework, then export zero-copy data pipelines to Core ML for seamless deployment.
Local LLMs & Multimodal Execution: Deploy heavyweights like Llama, Mistral, and Vision Transformers using 4-bit quantization, speculative decoding, and strict KV-cache management.
On-Device Fine-Tuning: Execute LoRA and QLoRA training loops directly on local GPUs, managing gradient checkpointing and batch sizes to prevent out-of-memory errors.
Platform-Native App Architecture: Isolate model inference from UI threads across iOS, macOS, and visionOS while ensuring strict user data privacy.
Deep Hardware Profiling: Use Instruments and the Metal Debugger to define latency contracts, track thermal limits, and hit a locked 30 FPS for real-time sensor processing.
Stop renting intelligence. Transform your M-Series hardware into a self-contained AI powerhouse and ship the high-demand, privacy-centric applications that the modern market demands.

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