GPU Finops The economics of AI compute, capacity and infrastructure [#1042217]

GPU Finops: The economics of AI compute, capacity and infrastructure
English | September 24, 2026 | ASIN: B0HKWW8FLF | 366 pages | Epub | 330.58 KB
GPU FinOpsThe Economics of AI Compute, Capacity, and InfrastructureAI infrastructure is expensive. But the hardest question is not simply how much the GPUs cost.It is understanding what that spending actually produces.A cluster can report high GPU utilization and still have poor economics. A more expensive accelerator can produce a lower cost per completed workload. Idle capacity can be intentional rather than wasteful. A lower cost per token does not automatically mean a lower cost per successful outcome.GPU FinOps provides a practical framework for understanding and managing the economics behind AI compute.Rather than treating GPU spending as a simple infrastructure bill, this book connects cost, capacity, utilization, performance, workload behavior, useful output, and business value into one economic system.You will learn how to:Understand the economic differences between GPU resources, capacity, allocation, utilization, and useful workBuild a meaningful GPU cost model instead of relying on a single price or utilization metricMeasure GPU economics using financial, infrastructure, workload, and performance telemetryCalculate the real cost of cloud GPU infrastructure and on-premises GPU environmentsAccount for shared infrastructure, indirect costs, total cost of ownership, and opportunity costEvaluate hardware and workload fit before making infrastructure decisionsAnalyze scheduling, placement, sharing, batching, and software optimization from an economic perspectiveDetermine whether an optimization actually creates economic value rather than merely reducing consumptionPlan future GPU capacity and understand the economics of reservations and commitmentsBuild allocation, showback, forecasting, and governance practices for shared GPU environmentsAnalyze the economics of training, fine-tuning, inference, and large language model workloadsDevelop workload-specific unit economics for meaningful comparisonsEstablish an operating model based on Identify → Measure → Attribute → Normalize → Model → Optimize → Validate → GovernThe book also includes practical reference material, including a GPU FinOps metric reference, cost-model worksheet, SQL and query reference, capacity-planning workbook, and decision checklists.From GPU Spending to GPU EconomicsThe central idea is simple:The price of compute is an input to economics, not the economic answer.A GPU-hour tells you how much accelerator time was consumed. It does not tell you whether that capacity was used effectively, what useful work it produced, what that work cost, or whether the resulting outcome justified the resources consumed.This book develops the reasoning required to answer those questions.It is not a CUDA programming guide, GPU architecture textbook, Kubernetes administration manual, AI model-development guide, cloud pricing catalog, or collection of cost-cutting tricks. Technical subjects appear when they affect the economics of GPU infrastructure.GPU FinOps is for engineers, FinOps practitioners, infrastructure and platform teams, technical leaders, architects, cloud and data-center teams, and organizations responsible for making AI compute economically sustainable.If your organization is buying, renting, allocating, operating, or planning GPU capacity, this book provides a framework for understanding what you are paying for, what you are getting from it, and how to make better-informed infrastructure decisions.