Run AI factory Kubernetes for enterprise teams. vMetal orchestrates machines, and vCluster combines separate tenant control planes with dedicated Private Nodes and central fleet operations.
Plain Kubernetes forces tradeoffs that slow an AI factory rollout.
Namespaces are too weak between tenants. A separate cluster per tenant is expensive and slow to provision at AI factory scale.
Building the provisioning, isolation and tenant tooling in-house takes engineering time most teams would rather spend on AI workloads.
In a shared cluster, tenants can see cluster-wide agents and other tenants' nodes. That's a security exposure for anyone handling customer data.
vMetal orchestrates GPU machines and vCluster supplies independent Kubernetes environments for AI factory teams. Private Nodes dedicate production workers, while Platform centralizes templates, access and fleet operations. vCluster Labs serves 50+ GPU clouds & Fortune 500s.
The layers an AI factory needs, from bare metal provisioning to AI environments and workload isolation with vNode.
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for AI factory teams; Private Nodes keep production workers tenant-specific.
Use supported workload templates for AI factory teams, 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.
vMetal exposes one Machine API above bare metal and VM provisioning drivers. Registered inventory, images and credentials enable repeatable machine lifecycle workflows for AI factory teams. Network automation uses supported integrations such as Netris.
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls. It adds runtime hardening for AI factory teams without a guest kernel or hypervisor, complementing dedicated workers rather than allocating GPUs.
Use tenant control-plane snapshots and documented backup workflows for AI factory teams. 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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An AI factory Kubernetes platform gives enterprise teams independent Kubernetes environments on the organization’s GPU infrastructure. vMetal orchestrates machines, and vCluster supplies tenant control planes, dedicated Private Nodes and central fleet management. Teams request approved environments through self-service templates. Kubernetes is one cluster type in the broader platform; workloads, GPU allocation and application readiness still require their own supported configuration.
Each tenant gets its own API server, data store, RBAC and CRDs, so tenants can't see each other's workloads or the platform internals. Private Nodes give each production tenant dedicated worker nodes with their own CNI and storage. Where you need a workload boundary as well, vNode adds one using Linux user namespaces and seccomp.
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.
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 Standalone runs as a Kubernetes binary directly on Linux without requiring another Kubernetes cluster, k3s or kubeadm. It can provide an initial Kubernetes foundation. vMetal is a separate infrastructure orchestration layer that requires vCluster Platform and a connected Kubernetes cluster for its Metal3/Ironic path. Hardware registration, images, credentials and driver setup remain prerequisites for machine provisioning.
vCluster is a CNCF-certified Kubernetes distribution and exposes standard Kubernetes APIs in tenant environments. This supports familiar clients, Helm charts and cluster-scoped resources such as CRDs. Certification covers Kubernetes conformance rather than an entire AI platform or customer deployment. Validate the selected version, networking, storage, GPU drivers and runtime integrations for the workloads you plan to support.
See how enterprises and GPU clouds run their AI factory on vCluster tenant clusters.
