Requesting nvidia.com/gpu: 2 treats accelerators as interchangeable device counts. Modern AI clusters need a richer contract for partitioned, topology-sensitive, or driver-managed devices. Kubernetes Dynamic Resource Allocation (DRA) provides that contract, but installing a DRA driver does not prove that real workloads can be admitted, allocated, run, and cleaned up correctly.

This session presents an upstream-only validation method for the path from ResourceClaim to accelerator compute. We separate readiness into six observable gates: driver availability, claim binding, Kueue admission, concurrent allocation, real GPU execution, and workload-controller lifecycle. We apply the same contract to a Job, JobSet, RayJob, and PyTorchJob to expose failures that installation checks miss.

The key lesson is that allocation and queue accounting are different correctness problems. Attendees leave with a reusable compatibility matrix, failure taxonomy, and rollout sequence for Kubernetes-based AI infrastructure.