Build an internal AI factory on your GPU estate. vMetal orchestrates machines, and vCluster gives production teams independent Kubernetes environments with dedicated Private Nodes.
Enterprises building internal AI factory infrastructure face three compounding challenges.
Standard Kubernetes forces you to choose between tenant isolation and operational efficiency: separate clusters are too expensive, namespace isolation too weak.
Building GPU infrastructure in-house requires significant engineering investment, and many teams spend years reaching production.
AI teams expect self-service Kubernetes and familiar tooling. Manual onboarding and unclear tenant boundaries make the infrastructure harder to use.
vMetal orchestrates enterprise GPU infrastructure. vCluster gives production teams independent Kubernetes clusters with dedicated Private Nodes, while Platform standardizes templates and access across the AI factory. vCluster Labs software powers 100K+ GPUs.
Every layer your AI factory needs, from bare metal provisioning and tenant isolation to pre-validated AI environments and Day 2 operations.
vMetal exposes one Machine API above bare metal and VM provisioning drivers. Registered inventory, images and credentials enable repeatable machine lifecycle workflows for enterprise AI infrastructure. Network automation uses supported integrations such as Netris.
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for enterprise AI infrastructure; Private Nodes keep production workers tenant-specific.
Use supported workload templates for enterprise AI infrastructure, including the NVIDIA Run:AI partner integration. Configure GPU, network and storage prerequisites, and validate the selected template for Private Nodes before offering it to production customers.
Karpenter-based Auto Nodes requests worker capacity according to configured policies. With the vMetal provider, Machine requests provision available infrastructure for enterprise AI infrastructure; inventory, driver readiness and release rules determine actual capacity.
Use tenant control-plane snapshots and documented backup workflows for enterprise AI infrastructure. Combine these with monitoring integrations and separate workload-data protection; recovery planning must include persistent volumes, connected infrastructure and restore testing.
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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AI factory infrastructure is the stack of compute, orchestration and isolation needed to run AI workloads at enterprise scale, from bare metal GPU servers through tenant environments to production AI platforms. vCluster Labs provides that stack: vMetal handles provisioning, vCluster orchestrates tenant clusters, and vNode helps prevent container breakout and limits blast radius. Teams get one integrated AI factory stack instead of assembling it from separate tools.
vCluster virtualizes the Kubernetes control plane. Each tenant or AI team gets its own API server, data store, RBAC and CRDs, all running as lightweight pods inside the control plane cluster. In production, Private Nodes give each team dedicated worker nodes with its own CNI and storage. You avoid the cost and lead time of separate physical clusters while getting a much stronger boundary than namespaces, and each extra tenant cluster costs little in control plane resources.
An enterprise AI factory launch depends on GPU inventory, provisioning drivers, networking, storage, identity and the workload environments teams need. vMetal orchestrates the machine layer, while vCluster provides tenant environments and self-service templates. Validate capacity, application readiness and operational ownership before rollout. The NVIDIA DGX reference architecture is the approved supporting asset for this audience.
vCluster is built for bare metal GPU infrastructure. vMetal handles zero-touch provisioning of Bare Metal Machines and VMs: PXE boot, OS installation, machine registration and lifecycle management. vCluster Standalone runs as a single binary on Linux, so you don't need k3s, kubeadm or k0s as a base layer. The stack runs 100K+ GPUs in production today.
NVIDIA Run:AI is available through the native Certified Stacks integration, with release-specific prerequisites and templates. Its bundled templates do not automatically supply dedicated Private Nodes. Configure and validate the production worker model, GPU access, networking and storage before offering it to customers. Ray and Jupyter can use compatible Kubernetes deployment tooling and require their own supported configuration.
vCluster layers its security controls for enterprise AI factory infrastructure. Control plane isolation gives each tenant its own Kubernetes API server and data store. vNode helps prevent container breakout and limits blast radius using user namespaces, seccomp and cgroups, without VM overhead. Hardware network isolation (VLANs, VXLANs, DPU policies) comes through the Netris integration with vMetal. Air-gapped and FIPS deployments are also supported for compliance-sensitive environments.
See how vCluster Labs powers enterprise AI factories on bare metal GPU infrastructure.
