Guide 
Platform Engineers, MLOps leaders, and AI Infrastructure teams: GPU costs are exploding—but most orgs are flying blind. Utilization dashboards look “healthy,” yet wasted capacity quietly stacks up as requests overshoot real consumption by 3× or more. The result? Premium GPU spend with disappointing yield, slower scaling, and constant firefighting.
This guide breaks down what’s actually driving GPU waste and how to fix it—starting with the right observability signals (GPU, VRAM, power, compute engines), then moving into the hard realities of sharing (MIG vs time-slicing), instance selection, and when “good enough” hardware wins. You’ll leave with a practical roadmap to automate right-sizing and align GPU spend with real business value. Reserve your copy today.
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