Behind every AI breakthrough lies a hidden foundation: a carefully engineered combination of hardware, software, and system architecture. This book illuminates that foundation. AI has evolved from a software-centric discipline into a full-stack systems problem. Performance, scalability, cost, and feasibility are no longer determined by neural network design alone, but by how well models are mapped onto GPUs, ASICs, memory hierarchies, interconnects, compilers, runtimes, and distributed systems. Understanding modern AI means understanding how these layers work together. AI Hardware, Software, and Architectures explains how modern AI systems actually run — from silicon to software — without vendor marketing, oversimplification, or abstract theory detached from real systems. It focuses on practical architectures, real performance constraints, and the engineering tradeoffs that shape training and inference at scale. Inside, you'll explore: GPUs, ASICs, and the accelerator hardware powering AI CUDA and its emerging alternatives Compilers, runtimes, and how code maps onto silicon Training systems and distributed-scale architectures Inference systems and real-world performance constraints The future of AI hardware, including quantum computing Whether you're an engineer, architect, researcher, student, or technical leader, this book gives you a clear, grounded understanding of the complete AI stack — the way it really works in production.
