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An analysis by The New Stack reveals that managing shared graphics processing units on Kubernetes does not provide the same level of visibility available for CPUs and memory, leaving platform teams to build measurement, cost-allocation, and control systems themselves. The problem is compounded by the fact that the GPU is the most expensive resource on the bill, while the accuracy of assigning compute consumption among workloads remains limited.