GPU Kubernetes Operations

Run resilient and cost-efficient GPU clusters for production AI workloads.

When to Use This Skill

• Setting up GPU node pools in Kubernetes for AI inference or training
• Configuring NVIDIA device plugin and GPU operator
• Implementing MIG partitioning to share GPUs across workloads
• Building GPU-aware autoscaling policies
• Monitoring GPU health with DCGM and Prometheus
• Troubleshooting GPU scheduling, driver, or OOM issues

All versions, profiles and metrics below are historical examples. Verify the current GPU/driver/CUDA/Kubernetes/operator compatibility matrix and exact supported MIG profiles. MIG reconfiguration, drains and driver changes disrupt workloads: require authorized maintenance scope, capacity and tested recovery. Time-slicing shares execution without MIG-style memory/fault isolation and does not multiply physical capacity.

Prerequisites