The NVIDIA GPU Operator is a Kubernetes-native operator that automates the deployment, configuration, and lifecycle management of GPU drivers, container runtimes, device plugins, and monitoring components across cluster nodes. It turns the complexity of GPU provisioning into declarative, reproducible workflows for training and inference workloads.
Automated GPU Stack Management
- Automated installation of NVIDIA drivers, Container Toolkit, and Device Plugin components eliminates manual node-level configuration
- Declarative Custom Resources manage driver and component versions, streamlining upgrades and rollbacks across the cluster
- Control-loop reconciliation maintains desired GPU stack state across all nodes following Kubernetes Operator best practices
Monitoring and Scheduling
- Integrated health monitoring and metrics exporters provide GPU visibility within Prometheus and other observability stacks
- Declarative configuration with node selectors and tolerations for targeted GPU scheduling
- Leverages Kubernetes Custom Resources and controller patterns to manage driver DaemonSets and related node-level resources
Training and Inference Workloads
- Running GPU-accelerated deep learning training clusters and inference services on Kubernetes infrastructure
- Managing heterogeneous GPU environments where standardizing drivers and runtimes reduces operational overhead
- GPU-based HPC jobs and data pipelines requiring consistent driver and runtime configuration across nodes