AI at Scale

"The definitive playbook for surviving the $100M infrastructure wall."Move beyond the Python code and master the brutal physics of trillion-parameter systems.Five years ago, "scaling AI" meant buying a bigger hard drive. Today, it means negotiating with local governments for megawatt power contracts and redesigning server racks for liquid cooling. When you cross the threshold of a trillion parameters, the comfortable abstractions of computer science melt away, leaving only the relentless realities of thermodynamics, memory bandwidth, and the speed of light.AI at Scale is the first physics-first field guide for the industrial era of Artificial Intelligence. Written for those who cannot afford to learn through expensive failures, this book starts where standard deep learning texts stop: the moment you sign a nine-figure purchase order for a 10,000-GPU cluster.Through real-world case studies like the silent $12 million thermal cascade and high-stakes infrastructure outages Manav Shrivastava reveals the "Invariant Laws of Scale" that govern the world’s most powerful foundation model labs. You will learn how to turn megawatts, GPUs, and petabytes of data into profitable, scalable intelligence.What You’ll LearnPredict the Future of Performance: Use Scaling Laws and the Predictive Performance Frontier to estimate model capability before spending a single GPU-hour.Navigate the Five Walls: Master the structural constraints: Thermal, Memory, Interconnect, Data, and Cost that dictate whether a training run converges or fails.Architect for Efficiency: Design multi-tier RAG hierarchies, optimize KV-cache management, and implement continuous batching to slash OpEx.Master GreenOps: Navigate the "Energy Wall" with carbon-aware scheduling and spatio-temporal workload shifting to reduce environmental impact.Bridge the Strategy Gap: Learn to justify nine-figure CapEx to boards and CFOs using TCO models and "Return on Compute" metrics.Who This Book Is ForThe Architect (CTO/VP): Learn to design sovereign AI strategies that survive hardware cycles and infrastructure debt.The Operator (ML Engineer): Master the 3 AM runbook for debugging 20,000-GPU clusters and "silent" hardware stragglers.The Builder (Infra Lead): Squeeze 3x throughput for inference and solve the "Small File Problem" in petabyte-scale pipelines.The Business Leader: Understand the physical limits of AI strategy without needing to be an equations expert. Read more