Part 1: Why Your Million-Dollar GPU Cluster is 80% Idle and how to fix it

Organizations face significant financial challenges due to GPU underutilization in Kubernetes clusters, often driven by the unpredictable nature of AI/ML workloads. Unlike CPUs, GPUs incur higher costs, making efficient utilization crucial for economic AI/ML infrastructure. Training workloads are particularly vulnerable to interruption costs, leading to resource overprovisioning; however, checkpoint/restore technology, such as CRIU-GPU, can mitigate this by allowing interrupted processes to resume efficiently. Real-time inference workloads are hindered by the cold start problem, where model loading delays lead to resource waste, emphasizing the need for strategic right-sizing of GPU instances to optimize utilization. Batch inference offers opportunities for improved resource efficiency through batching strategies, while research workflows struggle with irregular usage patterns and extended idle times, resulting in low utilization despite priority access to resources. Addressing these challenges requires tailored strategies for monitoring, optimizing, and architecting GPU usage to enhance return on investment and reduce waste in AI/ML operations.

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