Ray gives you actors, tasks and placement groups inside a cluster of pods that Kubernetes thinks are just pods. Two schedulers, two views of the same GPUs: the idle-node arithmetic, the placement group that cannot be gang-scheduled by the layer below, and the rules that stop the two from wasting each other's capacity.
Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.
Ray gives you actors, tasks and placement groups inside a cluster of pods that Kubernetes thinks are just pods. Two schedulers, two views of the same GPUs: the idle-node arithmetic, the placement group that cannot be gang-scheduled by the layer below, and the rules that stop the two from wasting each other's capacity.
Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.
Scored on saying clearly that Ray schedules work inside pods that Kubernetes schedules onto nodes, on the placement-group versus gang mismatch, and on giving a concrete waste number for the double-autoscaler problem.
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