ai-cloud
NVIDIA DGX Kubernetes for AI Cloud Providers
Deploy NVIDIA DGX with Kubernetes using vCluster to give every tenant isolated, CNCF-certified clusters on bare metal without performance loss or compliance risk.
Trusted by the fastest-growing AI cloud providers
Problem
The Real Cost of DGX at Scale
Common barriers blocking AI cloud providers from monetizing NVIDIA DGX infrastructure.
Isolation Without the Overhead
Namespace isolation is too weak. Separate physical clusters per tenant make NVIDIA DGX Kubernetes economics unworkable at scale.
Customers Expect Cloud Experiences
AI teams have used AWS and GCP. They expect self-service environments and managed Kubernetes, and they will leave if you cannot deliver.
DGX Investment Stalls at Launch
Building a GPU cloud platform yourself takes 6 to 10 engineers, 6 to 12 months, and over $1M in engineering cost.
Solution
vCluster Turns NVIDIA DGX Into a Managed Kubernetes Service
vCluster virtualizes the Kubernetes control plane so every tenant on your NVIDIA DGX cluster gets their own certified K8s API server, etcd, and RBAC as a lightweight pod. Named in the NVIDIA DGX SuperPOD reference architecture, vCluster powers 100K+ GPU nodes across 50+ GPU clouds and Fortune 500 customers.
Built for NVIDIA DGX Kubernetes Deployments
From bare metal provisioning to tenant isolation and workload security, every layer of the DGX Kubernetes stack is covered.
Bare Metal
Zero-Touch DGX Server Provisioning
vMetal handles PXE boot, OS installation, machine registration, and full GPU server lifecycle management so your NVIDIA DGX systems reach production with zero manual intervention.
- PXE boot and OS install automated
- GPU server lifecycle end-to-end
- Zero-touch rack to production
Tenant Isolation
Isolated Tenant Clusters on DGX Hardware
Each tenant on your NVIDIA DGX Kubernetes deployment gets a fully isolated control plane — own API server, etcd, and scheduler — running as a lightweight pod with no physical cluster overhead.
- Tenant clusters spin up in seconds
- Own API server and etcd per tenant
- No separate physical clusters needed
Hardware Isolation
Dedicated DGX Nodes Per Tenant
Assign fully dedicated physical DGX nodes to tenants with their own CNI and CSI. No workloads from other tenants share the hardware, delivering complete isolation at the infrastructure layer.
- Dedicated physical GPU nodes per tenant
- Own CNI and CSI per tenant
- No cross-tenant workload bleed
AI Readiness
Pre-Validated AI Stacks on DGX
Turn a bare NVIDIA DGX Kubernetes cluster into a production AI platform in minutes with pre-validated environments for Run:AI, Ray, and Jupyter — fully certified against tenant isolation.
- Run:AI, Ray, Jupyter pre-validated
- Cluster to AI platform in minutes
- Certified with tenant isolation
Workload Security
Kernel-Native Isolation Without VM Overhead
vNode (currently in private beta) provides container breakout protection using seccomp, cgroups, namespaces, and AppArmor — preserving bare metal NVIDIA DGX GPU performance while delivering strong per-workload security.
- No hypervisor tax on GPU performance
- Container breakout protection built in
- seccomp and AppArmor per workload
Why vCluster
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.
100K+
GPU Nodes Powered
50+
GPU Clouds & F500s
<45
Days to Launch
30K
GitHub Stars
Get Started in 3 Steps
1
Schedule a Demo
Talk to our team about your stack
2
Deploy vCluster
Deploy vCluster on your infra in minutes
3
Onboard Your Tenants
Go live with a hyperscaler-grade tenant experience in days
FAQs
What is NVIDIA DGX Kubernetes and why does it matter for AI cloud providers?
NVIDIA DGX Kubernetes refers to orchestrating NVIDIA DGX GPU servers with Kubernetes to manage and scale AI workloads. For AI cloud providers, it enables selling managed compute services on top of premium DGX hardware. vCluster is named in the NVIDIA DGX SuperPOD reference architecture, providing the tenant cluster orchestration layer that turns shared DGX infrastructure into isolated, customer-facing Kubernetes environments without provisioning a separate physical cluster per tenant.
How does vCluster integrate with the NVIDIA DGX SuperPOD architecture?
vCluster is named in the NVIDIA DGX SuperPOD reference architecture as the Kubernetes control plane virtualization layer. It allows operators to run hundreds of fully isolated tenant clusters on their DGX SuperPOD without the overhead of separate physical clusters. Each tenant gets a CNCF-certified Kubernetes environment with its own API server, etcd, and RBAC, running as a lightweight pod inside the host cluster on the DGX hardware.
Does running tenant clusters on NVIDIA DGX hardware impact GPU performance?
No. vCluster's control plane virtualization runs as a lightweight process with near-zero overhead, so tenant workloads access bare metal GPU performance directly. For workload-level isolation, vNode (currently in private beta) uses kernel-native mechanisms — seccomp, cgroups, namespaces, AppArmor — instead of a hypervisor, meaning there is no VM tax on your NVIDIA DGX GPU resources.
How quickly can an AI cloud provider launch a managed Kubernetes service on NVIDIA DGX?
Customers have launched managed Kubernetes services in as few as 45 days using vCluster, without dedicated platform engineering hires. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ tenant clusters. vCluster handles bare metal provisioning via vMetal, tenant cluster orchestration, and pre-validated AI stacks so your team reaches revenue faster on DGX hardware.
What isolation options are available for tenants on NVIDIA DGX Kubernetes?
vCluster offers a flexible isolation spectrum on NVIDIA DGX hardware. Shared Nodes provide namespace-level separation for cost efficiency. Private Nodes assign fully dedicated physical DGX nodes with isolated CNI and CSI per tenant. Dedicated Nodes eliminate GPU contention from other tenants. vNode (currently in private beta) adds kernel-native workload isolation using seccomp and AppArmor, with optional gVisor or Kata Containers layers for defense-in-depth security without hypervisor overhead.
Is vCluster compatible with compliance requirements for NVIDIA DGX deployments?
Yes. vCluster supports air-gapped and FIPS deployments for environments with strict compliance requirements. Control planes can run as VMs for OS-level separation when required by policy. Tenant network isolation is enforced through hardware-level VLANs, VXLANs, VRFs, and ACLs via Netris integration. Built-in Day 2 operations include observability, backups, and disaster recovery to support ongoing compliance across your DGX Kubernetes fleet.
Launch Your NVIDIA DGX Kubernetes Service
See how vCluster powers isolated tenant clusters on bare metal DGX infrastructure.
