ai-cloud
GPU Cluster Management for AI Clouds
vCluster Platform lets AI cloud providers deploy hundreds of fully isolated tenant clusters directly on bare metal, preserving full GPU performance without VM overhead.
Trusted by the fastest-growing AI cloud providers
Problem
The Real Cost of Weak Management
GPU cluster management breaks down at scale when isolation, automation, and speed are afterthoughts.
Isolation Without the Overhead
Namespace isolation is too weak. Separate physical clusters are too expensive. Standard Kubernetes forces you to choose between both.
GPU Performance Left on the Table
VM-based isolation adds hypervisor overhead that taxes GPU compute, cutting into customer experience and your margins.
Slow Time to Revenue
Building a GPU cloud platform in-house requires significant engineering investment.
Solution
Full Stack GPU Cluster Management on Bare Metal
vCluster Platform runs CNCF-certified tenant clusters as lightweight processes on bare metal, giving every customer their own API server, etcd, and RBAC. In production, the default is Private Nodes: dedicated worker nodes joined directly into each tenant cluster over an encrypted WireGuard VPN, with per-tenant CNI and storage. Powering 100K+ GPU nodes across 50+ GPU clouds and Fortune 500 customers.
GPU Cluster Management Built for Scale
From bare metal provisioning to tenant isolation to AI platform stacks, every layer is designed for GPU cloud providers running production workloads.
Bare Metal
Zero Touch GPU Server Provisioning
PXE boot, OS install, machine registration, and full GPU server lifecycle management from rack to production. No manual steps, no external dependencies.
- PXE boot to production-ready
- Full server lifecycle management
- Network automation via Netris
Tenant Isolation
Tenant Clusters as Lightweight Pods
Each tenant gets a fully isolated, CNCF-certified Kubernetes cluster running as a lightweight pod. Real API server, etcd, and scheduler, spinning up in seconds, not hours.
- Dedicated API server per tenant
- CNCF-certified K8s control plane
- Spins up in seconds
Workload Security
Kernel Native Workload Isolation
vNode prevents container breakouts using seccomp, cgroups, namespaces, and AppArmor, delivering strong workload isolation without a hypervisor tax on GPU performance.
- No hypervisor overhead
- Container breakout protection
- Bare metal GPU performance preserved
Dynamic Scaling
Automatic GPU Node Provisioning
Auto Nodes acts as bare metal Karpenter, automatically provisioning GPU servers via Terraform when tenants schedule workloads. Scale out physical GPU clusters without manual intervention.
- Automatic node provisioning via Terraform
- Scales bare metal GPU capacity
- Triggered by tenant workload demand
AI Platforms
Pre Validated AI Environment Stacks
Turn a bare Kubernetes cluster into a production AI platform in minutes with certified stacks that include partner integrations like Run:AI, Ray, and Jupyter. Certified to run inside isolated tenant environments.
- Run:AI, Ray, Jupyter ready
- Cluster to AI platform in minutes
- Certified with tenant isolation
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 makes vCluster different for GPU cluster management?
vCluster is the only platform that virtualizes the Kubernetes control plane itself, running each tenant cluster as a lightweight pod on bare metal. Unlike traditional approaches that provision separate physical clusters per tenant or rely on weak namespace isolation, vCluster gives every tenant a real, CNCF-certified Kubernetes API server with full cluster-admin access, without any VM overhead. This means strong control-plane isolation per tenant, and because vCluster runs as a lightweight process with no hypervisor, tenants get full bare metal GPU performance on the same physical hardware.
Can vCluster deploy tenant clusters directly on bare metal GPU servers?
Yes. vMetal handles zero-touch bare metal provisioning via PXE boot, OS installation, machine registration, and network automation. vCluster Standalone then runs as a single binary directly on Linux with no external Kubernetes dependency. Together they deliver a complete path from raw GPU racks to managed, isolated tenant clusters without needing k3s, kubeadm, or an intermediate Kubernetes layer.
How does vCluster handle tenant isolation on shared GPU hardware?
vCluster offers a flexible isolation spectrum: Private Nodes, the production default, deliver dedicated worker nodes with per-tenant CNI and storage for hardware-level separation. For cost-efficient dev, test, CI/CD, and trusted-team workloads, Shared Nodes provide flexible scheduling on shared infrastructure. vNode adds the strongest workload-level isolation: kernel-native process isolation using seccomp, cgroups, and AppArmor to prevent container breakouts and contain blast radius. Network isolation is enforced via hardware VLANs, VXLANs, and VRFs through the Netris integration.
How quickly can an AI cloud provider go live with vCluster?
Boost Run launched a managed Kubernetes offering in less than 45 days with a lean team. Lintasarta launched a GPU cloud in 90 days using vCluster Platform, running hundreds of tenant clusters at launch. These timelines are possible because vCluster eliminates the need to build control plane infrastructure, provisioning automation, and isolation layers from scratch.
Is vCluster production-proven at GPU scale?
Yes. vCluster powers 100K or more GPU nodes in production across 50 or more GPU cloud providers and Fortune 500 customers, including CoreWeave and Nscale. It has been used to create over 40 million tenant clusters and is named in the NVIDIA DGX SuperPOD reference architecture.
Does vCluster support GPU AI platform tooling like Run:AI or Ray?
Yes. Certified Stacks are AI environments that deploy partner integrations including Run:AI, Ray, and Jupyter on top of a bare Kubernetes cluster in minutes. These stacks are certified to run inside isolated tenant environments, so AI platforms operate with the same isolation guarantees as the underlying tenant cluster. This allows GPU cloud providers to offer managed AI platforms without additional integration work.
Launch Your GPU Cloud Faster
See how vCluster powers GPU cluster management for AI cloud providers at scale.
