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Version: v1.4

GPU Workload Placement

Nova can place GPU-accelerated workloads across one or more Kubernetes clusters, allowing workloads to run where suitable GPU resources are available.

When to Use This​

Use this pattern when:

  • GPU capacity is distributed across multiple clusters
  • Different clusters provide different GPU types or configurations
  • AI/ML workloads need to run where GPU resources are available
  • GPU workloads may need to move as capacity changes
  • Related application components should be co-located with GPU-backed services

How Nova Helps​

Nova evaluates placement policies and selects a workload cluster with sufficient GPU resources.

GPU-aware placement works with standard Kubernetes resource requests, including:

  • nvidia.com/gpu
  • amd.com/gpu
  • nvidia.com/mig-* (NVIDIA MIG mixed strategy)

Nova also respects workload constraints such as nodeSelector, which can be used to target specific GPU characteristics.

Considerations​

GPU placement depends on the workload clusters being prepared to run GPU workloads. This includes:

  • GPU-enabled nodes
  • Appropriate GPU drivers
  • GPU operators, such as the NVIDIA GPU Operator, where applicable
  • Accurate resource requests in workload manifests

Available GPU resources can be viewed through the Nova cluster inventory, for example by using:

kubectl  --context=nova get clusters -o wide

This will display GPU, CPU and Memory resources:

NAME    K8S-VERSION   K8S-CLUSTER    NOVA-CREATED   PROVIDER   REGION   ZONE       AVAIL-CPU   AVAIL-MEM     AVAIL-NVIDIAGPU   AVAIL-AMDGPU   READY   IDLE    STANDBY
wlc-1   1.35          worklc-12232   false          azure      eastus   eastus-2   16019m      102957284Ki   3                 0              True    False   False
wlc-2   1.35          worklc-30337   false          azure      eastus   eastus-2   12516m      91274704Ki    3                 0              True    False   False