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

If you’re running AI workloads on Kubernetes, chances are your average GPU utilization is below 20%, leading to thousands of dollars being wasted per cluster. 

In this hands-on workshop, we’ll show you how to measure what’s actually being used, uncover why GPUs go underutilized, and implement fixes that improve performance and unlock real efficiency gains.

You’ll discover:

• Why most GPU clusters run at just 15–25% utilization and how increasing that by even 10–20% can save hundreds of thousands in wasted compute
  
  
• How to go beyond nvidia‑smi, leveraging DCGM and Kubernetes integrations for granular GPU visibility
  
  
• Workload-specific optimization strategies like checkpoint/restore for training, right-sizing memory for inference, and cost‑effective node selection
  
  
• How NVIDIA MIG and container-level isolation let teams safely share GPUs without stepping on each other
  

You’ll walk away with understanding the resources required by workload type, concrete tools to measure GPU utilization and a clear roadmap for right-sizing your infrastructure.