25The cluster dashboard says 90% allocated and 30% utilized. What is happening, how do you prove it, and what policy fixes it?▼hardNewOpenAIAnthropicMeta4 replies◆ premiumAllocated means a scheduler handed the GPU out; utilized means it did work. A 60-point gap is jobs holding GPUs they do not use: idle notebooks, placeholder jobs, a loader-bound run at 20% tensor-active. The three metrics that separate the causes, the per-tenant table that names them, and the policies that close it.Open full answer →
27Implement dominant resource fairness for a cluster where jobs need different mixes of GPU, CPU and memory.▼hardNewOpenAIMeta4 replies◆ premiumFair sharing of one resource is obvious and fair sharing of three is not, because a GPU-heavy job and a memory-heavy job are not comparable. The rule that makes them comparable, the greedy loop that implements it, and the tie-breaking bug that gave one user everything.Open full answer →