Optimize GPU utilization with Kueue and KEDA

This blog post discusses how to optimize GPU utilization within the Red Hat OpenShift environment by integrating Kueue and the custom metrics autoscaler (KEDA). It highlights the importance of managing GPU resources effectively to avoid costs associated with idle GPUs, provides a proof of concept for using Kueue for AI workloads, and illustrates a detailed implementation guide including setup, submission of AI workloads, and dynamic scaling of resources based on GPU utilization metrics. The content aims at data scientists and developers working within cloud infrastructure and AI environments.