GPU and ML Workloads on Kubernetes: Scheduling, Sharing, and Monitoring

Calendar February 22, 2026
Kubernetes
Intermediate
Gpu-Scheduling, Ml-Infrastructure, Resource-Management, Workload-Isolation, Gpu-Monitoring
Gpu, Nvidia, Machine-Learning, Device-Plugin, Mig, Time-Slicing, Mps, Cuda, Node-Affinity, Taints, Dcgm
Kubectl, Nvidia-Smi, Helm, Dcgm-Exporter, Prometheus, Grafana

GPU and ML Workloads on Kubernetes#

Running GPU workloads on Kubernetes requires hardware-aware scheduling that the default scheduler does not provide out of the box. GPUs are expensive – an NVIDIA A100 node costs $3-12/hour on cloud providers – so efficient utilization matters far more than with CPU workloads. This article covers the full stack from device plugin installation through GPU sharing and monitoring.

The NVIDIA Device Plugin#

Kubernetes has no native understanding of GPUs. The NVIDIA device plugin bridges that gap by exposing GPUs as a schedulable resource (nvidia.com/gpu). Without it, the scheduler has no idea which nodes have GPUs or how many are available.