DGX SuperPOD Kubernetes for AI Cloud Providers
vCluster Platform, named in the NVIDIA DGX SuperPOD reference architecture, deploys hundreds of isolated, CNCF-certified tenant clusters on bare metal for maximum GPU utilization.
vCluster Platform, named in the NVIDIA DGX SuperPOD reference architecture, deploys hundreds of isolated, CNCF-certified tenant clusters on bare metal for maximum GPU utilization.
Raw DGX SuperPOD performance means nothing without the right Kubernetes orchestration layer.
Tenants can see platform internals they should not. Namespace isolation leaves every customer sharing the same blast radius.
Provisioning full physical clusters per tenant burns GPU capacity and delays onboarding by weeks, not hours.
AI teams have used AWS and GCP. They expect self-service Kubernetes and cloud-native tooling or they go back to a hyperscaler.
vCluster Platform virtualizes the Kubernetes control plane itself, running CNCF-certified tenant clusters as lightweight pods on your DGX SuperPOD bare metal. Every tenant gets their own API server, etcd, and RBAC in seconds, not weeks. Named in the NVIDIA DGX SuperPOD reference architecture, production-proven across 100K+ GPU nodes.
Purpose-built capabilities that turn DGX SuperPOD infrastructure into a production-grade managed Kubernetes service for AI cloud providers.
Each tenant gets a fully isolated Kubernetes control plane running as a lightweight pod on your DGX SuperPOD host cluster. Own API server, etcd, scheduler and RBAC — spinning up in seconds with near-zero overhead.

PXE boot, OS installation, machine registration, and full GPU server lifecycle management from rack to production. Automate the entire DGX SuperPOD provisioning workflow without manual intervention.

Every DGX SuperPOD tenant cluster is a fully CNCF-conformant Kubernetes distribution with 100% API compatibility. Tenants run any K8s workload without proprietary constraints or compatibility compromises.

Turn a bare DGX SuperPOD Kubernetes cluster into a production AI platform in minutes. Pre-validated integrations with Run:AI, Ray, and Jupyter are certified to work with tenant isolation out of the box.

Give high-value DGX SuperPOD tenants fully dedicated physical nodes with their own CNI and CSI. No workloads from other tenants share the same hardware, significantly reducing noisy-neighbor GPU contention.

This isn’t a side project. Behind every vCluster deployment is 5+ years of deep K8s engineering, security hardening, and battle-tested infrastructure work at massive scale.
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Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
Yes. vCluster Platform is named in the NVIDIA DGX SuperPOD reference architecture as a component for tenant cluster orchestration on bare metal GPU infrastructure. This means vCluster is named in the NVIDIA DGX SuperPOD reference architecture as a component for tenant cluster orchestration on bare metal GPU infrastructure, giving AI cloud providers confidence in the integration's architectural fit.
vCluster runs CNCF-certified tenant clusters as lightweight processes inside a host cluster on your DGX SuperPOD bare metal. Each tenant gets their own Kubernetes API server, etcd, scheduler, and RBAC without requiring separate physical clusters. vCluster Standalone can also run as a single binary directly on bare metal with no external Kubernetes dependency, eliminating the need for k3s or kubeadm as a base layer.
vCluster provides a spectrum of isolation options tailored to DGX SuperPOD deployments. At the control plane level, every tenant has a fully isolated API server and etcd. At the compute level, you can assign shared nodes, private dedicated nodes, or dedicated VMs per tenant. vNode (currently in private beta) adds kernel-native workload isolation using seccomp, cgroups, namespaces, and AppArmor, delivering container breakout protection without hypervisor overhead or GPU performance loss.
With vCluster Platform and vMetal bare metal provisioning, AI cloud providers have launched production managed Kubernetes services in weeks, not quarters. Boost Run launched their managed Kubernetes offering in less than 45 days with zero new platform engineering hires. Lintasarta launched Indonesia's leading GPU cloud in 90 days, running over 170 tenant clusters in production from day one.
vCluster's Certified Stacks include pre-validated integrations with Run:AI, Ray, and Jupyter that are certified to work with vCluster tenant isolation on DGX SuperPOD infrastructure. Slurm-on-Kubernetes via Slinky integration is also supported for teams running hybrid Slurm and Kubernetes workloads. These integrations turn a bare Kubernetes cluster into a production AI platform in minutes rather than weeks.
Yes. vCluster Platform includes a central UI, CLI, and API for managing all tenant clusters across your entire DGX SuperPOD fleet, including multi-region and multi-data-center deployments. Features include SSO, quotas, templates, auto-sleep, and full Day 2 operations such as observability, updates, backups, disaster recovery, and compliance management, all from a single control plane.
See how AI cloud providers deploy isolated tenant clusters on DGX SuperPOD bare metal.