Kueue and Volcano are leading the charge in orchestrating large-scale distributed ML jobs. But are they truly maximizing your GPU resources? Traditional batch scheduling methods often suffer from inefficient queue management, and rigid allocations that fail to adapt to real-time demand resulting in problems that scale with workloads.

This talk dives into how priority-aware queueing and elastic resource allocation can supercharge Kueue and Volcano, making batch scheduling more adaptive and efficient. We’ll break down the scheduler’s architecture, exploring how jobs dynamically move between priority queues, how elastic scheduling adjusts resource allocations in real time, and how these improvements lead to faster job execution and better GPU utilization.

Whether you're managing distributed training, hyperparameter tuning, or large-scale inference pipelines, this talk will provide the tools and strategies needed to unlock smarter scheduling and maximize ROI on Kubernetes GPU workloads.