The Kubernetes scheduler, which ensures pods are matched with the right nodes for optimal performance and availability, has limitations that can lead to increased cloud expenses when running GPU-intensive workloads. To overcome this challenge, CAST AI's node templates offer a solution by allowing users to define specific requirements for their instances, such as memory-optimized and GPU VMs, and automatically select matching instances. This feature also enables users to utilize different instance types more freely, avoid expensive resources after GPU jobs are completed, and save up to 90% on GPU VM costs using spot instance automation. By creating node templates, users can achieve better performance and flexibility for their GPU-intensive workloads while reducing costs.