GPU Operator, Kueue, multi-tenant

GPUs shared cleanly across your teams, with zero waste

A GPU at 30% utilization is an expense, not an investment. A well-designed Kubernetes farm isolates teams, enforces quotas and pushes real fleet utilization to the maximum.

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Use cases

Internal AI platform shared across data science and product teams
Production inference: autoscaling, rolling updates, high availability
On-demand GPU development environments (notebooks, jobs)
Multi-customer platform for SaaS vendors and AI service providers

Reference architecture

GPU: NVIDIA GPU Operator: drivers, device plugin, DCGM monitoring and time-slicing / MIG for fine-grained sharing.
Scheduling: Kueue or Volcano for batch queues, team quotas and controlled preemption.
Multi-tenant: Namespaces, RBAC, network policies and, if needed, virtualized control planes per tenant.
Platform: GitOps (Argo CD), private registry, ingress, cert-manager and Prometheus / Grafana observability.

What you receive

01
Hardened GPU Kubernetes cluster, versioned with GitOps
02
Quotas, queues and per-team isolation configured
03
GPU utilization dashboards per team and per project
04
Runbook, training and full handover

FAQ

Can Kubernetes replace Slurm for training?: For inference and single-node jobs, yes, without reservation. For large-scale multi-node training, Slurm keeps the native gang scheduling advantage; Kueue and Volcano close the gap at intermediate scales. We size it based on your jobs.
Do you handle strict multi-tenancy between customers?: Yes: network isolation, dedicated control planes per tenant when necessary, hard quotas and consumption-based billing. That is the foundation of the multi-customer AI platforms we deploy.

Request a technical audit

Reference architecture

GPU: NVIDIA GPU Operator: drivers, device plugin, DCGM monitoring and time-slicing / MIG for fine-grained sharing.
Scheduling: Kueue or Volcano for batch queues, team quotas and controlled preemption.
Multi-tenant: Namespaces, RBAC, network policies and, if needed, virtualized control planes per tenant.
Platform: GitOps (Argo CD), private registry, ingress, cert-manager and Prometheus / Grafana observability.

FAQ

Can Kubernetes replace Slurm for training?

For inference and single-node jobs, yes, without reservation. For large-scale multi-node training, Slurm keeps the native gang scheduling advantage; Kueue and Volcano close the gap at intermediate scales. We size it based on your jobs.

Do you handle strict multi-tenancy between customers?

Yes: network isolation, dedicated control planes per tenant when necessary, hard quotas and consumption-based billing. That is the foundation of the multi-customer AI platforms we deploy.