GPU Cost Efficiency in Kubernetes: Selection, Sharing, and Savings Strategies

Engineering teams can reduce GPU costs in Kubernetes by implementing strategies such as right-sizing GPU instances, enabling autoscaling, and leveraging sharing methods like time slicing or NVIDIA Multi-instance GPUs (MIGs). Kubernetes simplifies GPU management compared to standalone GPU VMs, offering better cost visibility, workload scaling, and job management. The major cloud providers—AWS, Azure, and GCP—offer different GPU pricing structures, typically based on instance-hour usage, which can lead to over-provisioning and idle capacity without proper cost-saving measures. By selecting appropriately sized instances and utilizing tools like the Vantage Kubernetes agent, teams can better measure GPU memory usage and avoid unnecessary expenses. Additionally, committing to Savings Plans can further decrease costs compared to On-Demand pricing, while sharing strategies allow multiple workloads to efficiently use GPU resources, particularly for smaller models or batch inference jobs.