Modern AI workloads expose two gaps in Kubernetes scheduling. First, accelerators are no longer simple device counts: a MIG 1g.10gb slice and a 7g.80gb partition may count as “1 GPU” despite using vastly different capacity. Second, AI jobs need gang scheduling and topology-aware placement. kube-scheduler addresses these through Dynamic Resource Allocation (DRA) for accelerator modeling and Workload-Aware Scheduling (WAS) for workload-level placement. But clusters need quota and queuing, which Kueue provides.

We present Kueue’s integration with DRA, using Partitionable Devices to charge quota in units like GPU memory, not device count. MIG profiles sharing one quota pool can be charged by actual use.

Finally, we introduce scheduler-library, exposing kube-scheduler logic as compile-time building blocks for orchestrators like Kueue and Cluster Autoscaler. For Kueue, it combines scheduler-native accuracy with fast scheduling and preemption simulations for multi-tenant quota management.