GPU Cluster on Kubernetes
Running GPU workloads on Kubernetes requires more than adding GPU nodes. You need device plugins, proper scheduling, node autoscaling, resource quotas, and cost controls. We build GPU clusters that maximize hardware utilization while keeping your cloud bill predictable.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
GPU Node Configuration
GPU node pools get configured with the NVIDIA GPU Operator — handling driver installation, container toolkit, device plugin, and DCGM monitoring automatically. Node labels and taints ensure only GPU workloads schedule on expensive GPU nodes. We configure MIG (Multi-Instance GPU) on A100s to partition a single GPU into up to 7 isolated instances for inference workloads that don't need a full GPU.
Scheduling & Resource Management
GPU resource requests and limits prevent oversubscription. Topology-aware scheduling places multi-GPU pods on nodes with NVLink connectivity. Priority classes ensure training jobs can preempt lower-priority inference workloads during peak hours. Gang scheduling via Volcano or Kueue ensures distributed training jobs get all their GPUs simultaneously or not at all.
Autoscaling & Spot Instances
Cluster Autoscaler provisions GPU nodes on demand — scaling from zero for batch training and back down when idle. Spot/preemptible instances cut GPU costs by 60-70% for fault-tolerant training jobs. We configure checkpointing and preemption handling so spot interruptions cause minimal wasted work.
Cost Monitoring & Optimization
Kubecost or OpenCost tracks per-team, per-job GPU spend. Dashboards show GPU utilization rates — if GPUs sit idle, you're burning money. We configure right-sizing recommendations based on actual utilization data. Resource quotas per namespace prevent any single team from monopolizing the cluster.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.