{"product_id":"engineering-ai-on-apple-silicon-unified-memory-metal-compute-mlx-and-core-ml-for-on-device-intelligence-9798259076129","title":"Engineering AI on Apple Silicon: Unified Memory, Metal Compute, MLX, and Core ML for On-Device Intelligence","description":"\u003cp\u003e • Author(s): Albert V. Chitwood\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Macintosh\u003c\/p\u003e\u003cp\u003eStop Paying for Cloud AI Compute. Master Apple Silicon and Build High-Performance, Privacy-First AI On-Device.\u003cbr\u003eThe 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.\u003cbr\u003eEngineering 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.\u003cbr\u003eInside, you will discover: \u003cbr\u003eUnified Memory Mastery: Stop treating a Mac like a standard PC. Learn how shared address spaces eliminate PCIe bottlenecks to unlock unprecedented inference throughput.\u003cbr\u003eThe 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.\u003cbr\u003eLocal LLMs \u0026amp; Multimodal Execution: Deploy heavyweights like Llama, Mistral, and Vision Transformers using 4-bit quantization, speculative decoding, and strict KV-cache management.\u003cbr\u003eOn-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.\u003cbr\u003ePlatform-Native App Architecture: Isolate model inference from UI threads across iOS, macOS, and visionOS while ensuring strict user data privacy.\u003cbr\u003eDeep 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.\u003cbr\u003eStop 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.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47883299848343,"sku":"9798259076129","price":2363.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798259076129.webp?v=1781100946","url":"https:\/\/atlanticbooks.com\/products\/engineering-ai-on-apple-silicon-unified-memory-metal-compute-mlx-and-core-ml-for-on-device-intelligence-9798259076129","provider":"Atlantic Books","version":"1.0","type":"link"}