Kubernetes GPU Optimization: How to Cut GPU Waste Without Slowing Workloads

The text discusses GPU optimization in Kubernetes, emphasizing the importance of maximizing GPU usage through techniques such as partitioning, sharing, intelligent scheduling, and node lifecycle automation. Despite these optimization strategies, the Cast AI 2026 State of Kubernetes Optimization report reveals that average GPU utilization in production clusters is only 5%, significantly lower than CPU and memory utilization. The text introduces a diagnostic framework called "The Four GPU Money Leaks" that identifies key areas of inefficiency and offers targeted solutions, such as scale-to-zero autoscaling and MIG partitioning, to reduce GPU costs and increase utilization. Different methods like MIG, time-slicing, and Dynamic Resource Allocation (DRA) are compared for their suitability in various scenarios, while the text also highlights the operational challenges of integrating these techniques effectively. Cast AI's approach to addressing the coordination gap through an automated GPU optimization stack is presented as a way to achieve up to 90% GPU cost reductions for developers by combining these strategies under a single control plane.

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