Running AI workloads on Google Kubernetes Engine (GKE) presents unique challenges, especially for securing the right hardware. Whether you’re dealing with unpredictable demand and varying job durations or simply looking to control costs, this session will equip you with the knowledge and tools to make informed decisions about your GKE AI infrastructure. We’ll explore recent advancements in Dynamic Workload Scheduler, custom compute classes, and Kueue, demonstrating how these technologies can help you effectively access and manage diverse hardware resources.