Kueue, the Kubernetes-native workload orchestrator, helps run AI workloads at scale with multi-tenant quota management and advanced scheduling.

In this session, we show through a case study at the Meta Superintelligence Lab how Kueue provided the right quota and scheduling layer for large-scale AI training and inference across complex, heterogeneous GPU topologies, enabling a shift from an in-house platform to an open-source stack on Kubernetes.

We then dive into Kueue’s multi-layer Topology-Aware Scheduling (TAS) for modern clusters with hierarchical network fabrics. We cover the main concepts and algorithms behind Hierarchical TAS, and show why multi-layer TAS was critical for this transition.

We close with other cutting-edge Kueue capabilities, including Workload-Aware Scheduler integration, elastic workloads, and multi-cluster job scheduling with MultiKueue.