Google GKE
Google Cloud
Managed Kubernetes on GCP. GKE can auto-install NVIDIA drivers on GPU node pools via a DaemonSet, or you can run the GPU Operator.
Bootstrap with Google GKEGKE driver DaemonSet or NVIDIA GPU Operator
GKE installs drivers via a Google-managed DaemonSet when the node pool has accelerators; Autopilot handles it automatically.
containerd with NVIDIA runtime.
Default scheduler; accelerator node pools; cluster autoscaler; Autopilot GPU pods.
MIG and time-slicing supported; configure on the node pool or via the operator.
GKE Dataplane V2 (Cilium); GPUDirect-TCPX/RDMA on A3 for high-throughput training.
Node auto-upgrade; pin driver version if a workload needs a specific CUDA.
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Create a GPU node pool
Attach accelerators and let GKE install the driver.
bashgcloud container node-pools create gpu-pool \ --cluster $CLUSTER --accelerator type=nvidia-h100-80gb,count=8 \ --machine-type a3-highgpu-8g --num-nodes 2 - 2
Install the driver DaemonSet (if not automatic)
Applies Google's driver installer to accelerator nodes.
bashkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml - 3
Request a GPU in the pod spec
Set limits: nvidia.com/gpu on the container.
kubectl get nodes -o custom-columns=NAME:.metadata.name,GPU:.status.allocatable.'nvidia\.com/gpu'
kubectl -n gpu-operator get pods
kubectl run cuda-check --rm -it --restart=Never \
--image=nvidia/cuda:12.4.1-base-ubuntu22.04 \
--limits=nvidia.com/gpu=1 -- nvidia-smi- ·GPUDirect-TCPX/RDMA is limited to specific machine families (A3).
- ·Driver DaemonSet vs operator: pick one to avoid conflicts.
GCP platforms wanting minimal driver management, and A3 for large training.