kueue
AI infra interview questions tagged kueue, across every topic.
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Concepts behind "kueue"
The curriculum that explains the ideas these questions test.
Foundational
Slurm vs KubernetesEvery GPU platform team has this argument, and the two schedulers were built for different jobs: Slurm for long, large, all-or-nothing training on bare metal; Kubernetes for many services that scale up and down. Training fleets run Slurm because gang scheduling, topology and MPI-style launch are native there; serving fleets run Kubernetes because autoscaling and rolling deploys are native there. A platform that does both picks a hybrid: Slurm on Kubernetes (Slinky, Soperator) or a batch scheduler on Kubernetes (Kueue, Volcano, KAI). The interview question is which, for which workload, and why.🗂️ Scheduling & Orchestration
Advanced
Gang Scheduling with Kueue and VolcanoA distributed training job is 64 pods that start together or not at all: if 40 are running and 24 are Pending, the 40 hold their GPUs idle at a collective barrier waiting for ranks that may never come, and two such jobs can deadlock a whole cluster. Gang scheduling makes the job the unit of admission. Kueue and Volcano add queues, quotas, priorities and preemption on top, which is what turns a pile of GPUs into a platform several teams can share without starving each other.🗂️ Scheduling & Orchestration🔒 Premium
Advanced
Topology-Aware SchedulingTwo placements of the same 64-GPU job can differ by 2x in step time: one keeps every tensor-parallel group on a single NVSwitch node and every data-parallel ring on a single rail, the other scatters ranks across racks and pushes per-layer traffic through the spine. The scheduler is the only thing that can prevent the second placement, because the framework maps ranks to whatever GPUs it is handed. Topology-aware scheduling means the scheduler knows the hierarchy (NVLink domain, rail, rack, spine block) and places gangs to keep traffic low in it.🗂️ Scheduling & Orchestration🔒 Premium