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How to cut AI infrastructure spending without reducing GPU capacity
Every infrastructure leader running AI workloads is staring at the same problem: GPU spending keeps climbing, the finance team wants a justification, and the operations team is caught between proving the infrastructure is necessary and explaining why the returns aren't keeping pace with the investment. The instinctive response is to either procure more capacity to handle growing demand or cut back on what's already deployed. Neither actually solves the problem.