How to Share GPUs Fairly Across Teams with Kubernetes Kueue (Instead of Buying More GPUs)

Kubernetes Kueue is presented as a Kubernetes-native admission controller for sharing scarce GPU capacity across teams by queueing workloads until their full CPU, memory, and GPU requirements can be reserved, then allowing the standard kube-scheduler to place their pods. It addresses idle-but-reserved GPU pools, fragmented distributed-training jobs, and the lack of borrowing in standard ResourceQuota through ResourceFlavors for hardware types, ClusterQueues for team quotas, LocalQueues for namespace entry points, and cohorts that enable controlled borrowing, lending, and preemption. Teams receive guaranteed nominal GPU allocations while being able to use unused capacity from others, with owners able to reclaim their quota when needed; priorities, fair-sharing weights, checkpointing, topology-aware scheduling, and spot-capacity flavors can further shape behavior. Kueue does not partition physical GPUs, provision nodes, replace Kubernetes scheduling, or operate cluster infrastructure, so technologies such as MIG, MPS, time-slicing, autoscalers, GPU operators, and platform tooling remain separate concerns. The guide recommends measuring real GPU utilization with DCGM alongside Kueue queue depth and admission wait times before purchasing additional hardware, arguing that low utilization combined with long waits indicates a scheduling and governance issue, whereas sustained high utilization and long waits after tuning quotas may justify more capacity.

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