The New Stack: Before you expand your GPU pool, measure the outcome
The article argues that teams running AI workloads on Kubernetes should define outcomes, deadlines, and costs before expanding a GPU pool. It suggests staggering batch jobs with scheduling flexibility, load-testing sustainable serving capacity, and joining OpenCost allocation data with verified outcome records to compute cost per verified outcome.
What changes for platform teams
Platform teams can use OpenCost allocation data joined with serving telemetry and outcome records to decide between scheduling changes, batching, resizing, model changes, or new GPUs. The article notes OpenCost proposal 3915 for serving-engine telemetry is still a draft.
Read the original at The New Stack ↗
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