💡 Quick Answer: Configure NVIDIA GPU Feature Discovery for automatic node labeling on Kubernetes. GPU model, driver version, CUDA, and MIG labels for scheduling.

The Problem

Configure NVIDIA GPU Feature Discovery for automatic node labeling on Kubernetes. Without proper setup, GPU workloads on Kubernetes suffer from wasted resources, failed scheduling, or degraded inference performance.

The Solution

Prerequisites

# Verify GPU nodes are available
kubectl get nodes -l nvidia.com/gpu.present=true
kubectl describe node <gpu-node> | grep -A5 "Allocatable"

# Check NVIDIA driver and CUDA
kubectl exec -it <gpu-pod> -- nvidia-smi

Configuration

# GPU Feature Discovery Node Labels — production configuration
apiVersion: v1
kind: Pod
metadata:
  name: gpu-workload
  namespace: gpu-inference
spec:
  containers:
  - name: inference
    image: nvcr.io/nvidia/pytorch:24.07-py3
    resources:
      limits:
        nvidia.com/gpu: 1
      requests:
        nvidia.com/gpu: 1
    env:
    - name: NVIDIA_VISIBLE_DEVICES
      value: "all"
    - name: NVIDIA_DRIVER_CAPABILITIES
      value: "compute,utility"
  nodeSelector:
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

Deployment

# Apply GPU workload
kubectl apply -f gpu-workload.yaml

# Verify GPU allocation
kubectl describe pod gpu-workload | grep -A3 "Limits"

# Monitor GPU utilization
kubectl exec -it gpu-workload -- nvidia-smi dmon -s pucvmet -d 5

Verification

# Check GPU is accessible inside the pod
kubectl exec -it gpu-workload -- python3 -c "
import torch
print(f'CUDA available: {torch.cuda.is_available()}')
print(f'GPU count: {torch.cuda.device_count()}')
print(f'GPU name: {torch.cuda.get_device_name(0)}')
print(f'Memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB')
"

graph TD
    A[GPU Node] --> B[NVIDIA Driver]
    B --> C[Container Toolkit]
    C --> D[Device Plugin]
    D --> E[Pod GPU Access]
    E --> F{Inference / Training}
    F --> G[Monitor with nvidia-smi]
    G --> H[Scale with HPA/KEDA]

Common Issues

GPU not visible inside pod

Check that the NVIDIA device plugin DaemonSet is running on the node. Verify with kubectl get pods -n gpu-operator -l app=nvidia-device-plugin-daemonset. If missing, the GPU Operator may need reinstalling.

CUDA version mismatch

The container CUDA version must be compatible with the host driver. Use nvidia-smi on the node to check driver version, then select a compatible container image from NVIDIA NGC catalog.

Out of memory on GPU

Reduce batch size, enable gradient checkpointing for training, or use model quantization (AWQ/GPTQ) for inference. Monitor with nvidia-smi to track peak memory usage.

Best Practices

Always set resources.limits for nvidia.com/gpu — without it, pods won’t get GPU access
Use node selectors or affinity to target specific GPU types (A100, H100, etc.)
Monitor GPU utilization with DCGM Exporter + Prometheus — idle GPUs waste expensive resources
Pin CUDA container versions — don’t use latest tags in production
Enable GPU health checks with liveness probes that verify CUDA functionality

Key Takeaways

GPU Feature Discovery Node Labels is critical for production GPU workloads on Kubernetes
Proper resource configuration prevents scheduling failures and resource waste
Monitor GPU utilization to right-size allocations and reduce cloud costs
Use NVIDIA GPU Operator for automated driver and toolkit lifecycle management
Combine with KEDA or custom metrics HPA for GPU-aware autoscaling