Deliver isolated, CNCF-certified tenant clusters on dedicated or shared GPU infrastructure. For production, Private Nodes give each tenant dedicated worker nodes with per-tenant CNI and storage: hardware-level isolation at cloud scale.
The real bottleneck is not hardware. It is the infrastructure layer above it.
Selling bare metal GPUs alone is a race to the bottom. Customers want the cloud experience your hyperscaler competitors already offer.
Boost Run launched a managed Kubernetes product in under 45 days using the vCluster Platform.
Standard Kubernetes forces you to choose. Namespace isolation is too weak. Separate physical clusters are too expensive and too slow.
vCluster is the only platform we know of that integrates bare metal provisioning, CNCF-certified tenant clusters, and workload isolation in a single purpose-built stack for AI cloud providers. Boost Run launched managed Kubernetes in under 45 days. Lintasarta launched a GPU cloud in Indonesia in 90 days.
Every layer of the stack purpose-built for AI cloud providers running GPU workloads at scale across hundreds of tenant environments.
Each tenant gets a real Kubernetes API server, etcd, scheduler, and RBAC running as lightweight pods on shared GPU infrastructure. For production, Private Nodes give each tenant dedicated worker nodes with per-tenant CNI and storage, eliminating shared blast radius.
vMetal handles PXE boot, OS installation, network automation via Netris, and full machine lifecycle management. Go from GPU rack to a production-ready Kubernetes base in a single integrated workflow.
Auto Nodes acts as bare metal Karpenter, automatically provisioning GPU servers via Terraform when tenants schedule workloads. Scale physical GPU capacity dynamically without manual intervention.
Pre-validated partner integrations for Run:AI, Ray, and Jupyter help turn a bare Kubernetes tenant cluster into a production AI platform. Each stack is certified to work within isolated tenant environments: partner integrations like Run:AI come pre-tested, reducing time to production.
vNode delivers container breakout protection using seccomp, cgroups, namespaces, and AppArmor at the kernel level. Strong workload isolation at bare metal GPU performance with no hypervisor tax.
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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Go live with a hyperscaler-grade tenant experience in days
vCluster is purpose-built for the full GPU infrastructure stack. It covers bare metal provisioning with vMetal, tenant cluster orchestration with the vCluster Platform, and workload isolation with vNode. Unlike generic Kubernetes tools, the entire stack is designed for AI cloud providers running GPU workloads at scale, and it is production-proven across 100K+ GPU nodes and 50+ GPU clouds and Fortune 500 customers.
Each tenant gets a fully isolated Kubernetes control plane running as a lightweight pod inside a shared control plane cluster. This includes a dedicated API server, etcd, scheduler, and RBAC. Tenants cannot see each other's nodes, pods, or platform internals. The isolation model defaults to Private Nodes, dedicated worker nodes with per-tenant CNI and storage for full hardware separation, and also supports Shared Nodes with resource quotas for dev, test, and trusted-team workloads, depending on what the tenant requires.
Yes. vCluster Standalone runs as a binary directly on bare metal Linux with no dependency on k3s, kubeadm, or any external Kubernetes distribution. Combined with vMetal for zero-touch provisioning, the stack takes GPU servers from PXE boot through OS installation, network configuration, and into a production Kubernetes environment without any intermediate layers.
Boost Run launched a managed Kubernetes product in under 45 days using the vCluster Platform. Lintasarta launched a GPU cloud in Indonesia with hundreds of tenant clusters in 90 days.
Yes. Every tenant cluster provisioned by the vCluster Platform is a fully CNCF-certified Kubernetes control plane with 100 percent API compatibility. Tenants receive full cluster-admin access to install their own CRDs, configure RBAC, and run any standard Kubernetes tooling. vCluster is named in the NVIDIA DGX SuperPOD reference architecture.
The Certified Stacks feature includes pre-validated partner integrations for Run:AI, Ray, and Jupyter, as well as Slurm-on-Kubernetes support via Slinky integration. These stacks are tested and certified against vCluster tenant environments, so AI training and inference workloads can be deployed in isolated environments with a known-good configuration baseline.
See how AI cloud providers go from GPU racks to managed Kubernetes in weeks.
