Kubernetes GPU Autoscaling: Scale GPU Capacity to Real Demand

GPU autoscaling in Kubernetes is a critical strategy for optimizing AI infrastructure costs, given that GPU utilization averages only 5% across production clusters, while on-demand costs for GPUs like the AWS H100 can reach $12.30 per hour. This inefficiency is exacerbated by rising GPU prices, such as the 15% increase in AWS's H200 Capacity Block pricing in 2026. Tools like Karpenter, Cluster Autoscaler, HPA (Horizontal Pod Autoscaler), and KEDA (Kubernetes Event-Driven Autoscaling) enable dynamic scaling of GPU nodes and pods, responding to workload demands by provisioning resources during high demand and scaling down, even to zero, when idle. Karpenter is noted for its speed and flexibility in managing GPU instances, while KEDA excels in scaling to zero by using request queue depth as a metric, ideal for spiky inference workloads. Effective GPU autoscaling requires integration of node-level and pod-level autoscaling, leveraging techniques like GPU time-slicing and Spot instance utilization to achieve cost savings of over 70% compared to traditional setups, as evidenced by case studies like ALLEN Digital's transition to Kubernetes.

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