Kubernetes clusters designed and operated for GPU workloads.
Running GPU workloads on Kubernetes reliably requires correct GPU scheduling, driver management and observability — details that differ significantly from standard CPU-based clusters. GPUMarket designs, deploys and operates GPU-aware Kubernetes clusters end to end.
What this involves
- NVIDIA GPU Operator and device plugin configuration
- GPU-aware scheduling and bin packing
- Cluster autoscaling for GPU node pools
- GPU metrics and observability (utilization, memory, temperature)
- Storage integration for model and dataset access
- Multi-tenant GPU sharing where appropriate
Common challenges we help solve
Correctly exposing and scheduling GPUs in Kubernetes
We configure the NVIDIA GPU Operator and device plugin, and validate scheduling behavior before go-live.
Getting visibility into GPU utilization and health
We deploy GPU-aware monitoring so utilization, memory and errors are visible alongside standard cluster metrics.
Scaling GPU node pools without wasting capacity
We configure autoscaling policies tuned to GPU workload characteristics rather than generic CPU heuristics.
Recommended GPUs
Related managed software
Frequently asked questions
Can you migrate our existing GPU workloads to Kubernetes?
Yes. We can design a GPU Kubernetes cluster and help migrate existing training or inference workloads, including validating scheduling and performance before cutover.
Do you support managed Kubernetes services or only self-managed clusters?
We work with both managed Kubernetes offerings from our provider network and self-managed clusters on dedicated infrastructure, depending on your requirements.