GPU Optimization
Why Your Million-Dollar GPU Cluster is 80% Idle and How to Fix It
October 22, 20251 min read
Most GPU clusters run below 20% average utilization, resulting in massive waste of expensive compute resources. This hands-on workshop dives deep into why this happens and provides actionable strategies to improve GPU efficiency for AI workloads on Kubernetes.
What You'll Learn#
- 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
Who Should Attend#
Platform engineers, DevOps teams, and engineering leaders managing GPU infrastructure for AI/ML workloads on Kubernetes.
Speakers
Debosmit Ray