ai-cloud
GPU Cluster Management for AI Clouds
Turn GPU servers into customer environments. vMetal orchestrates machine provisioning, and vCluster combines separate tenant control planes with dedicated worker capacity through Private Nodes.
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.
Tenant Isolation Adds Operational Work
Operators often choose between one shared control plane and separately operated clusters. Dedicated tenant boundaries need a manageable infrastructure model.
GPU Infrastructure Requires Careful Configuration
GPU throughput depends on drivers, device access, networking and workload configuration. Bare metal removes a hypervisor layer but still needs validation.
Slow Time to Revenue
Building a GPU cloud platform in-house requires significant engineering investment.
Solution
GPU Cluster Management from Machines to Tenants
vMetal orchestrates the machine lifecycle across bare metal and VMs. vCluster provides separate tenant control planes and dedicated workers through Private Nodes, with optional vNode runtime hardening. vCluster Labs serves 50+ GPU clouds & Fortune 500s.
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
vMetal exposes one Machine API above bare metal and VM provisioning drivers. Registered inventory, images and credentials enable repeatable machine lifecycle workflows for GPU cluster management. Network automation uses supported integrations such as Netris.
- Stable Machine API
- Bare metal and VM provisioning
- Driver-specific lifecycle automation
Tenant Isolation
Tenant Clusters as Lightweight Pods
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for GPU cluster management; Private Nodes keep production workers tenant-specific.
- Independent tenant API and data store
- Tenant-scoped scheduling on Private Nodes
- No dedicated control-plane servers per tenant
Workload Security
Kernel Native Workload Isolation
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls. It adds runtime hardening for GPU cluster management without a guest kernel or hypervisor, complementing dedicated workers rather than allocating GPUs.
- Linux user namespaces and seccomp
- No guest kernel or hypervisor
- Helps limit workload breakout
Dynamic Scaling
Automatic GPU Node Provisioning
Karpenter-based Auto Nodes requests worker capacity according to configured policies. With the vMetal provider, Machine requests provision available infrastructure for GPU cluster management; inventory, driver readiness and release rules determine actual capacity.
- Karpenter-based node management
- Supported infrastructure provider integration
- Inventory-aware capacity policies
AI Workload Integration
Supported AI Workload Templates
Use supported workload templates for GPU cluster management, 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.
- Native NVIDIA Run:AI catalog
- Validate Private Node configuration
- Release-specific integration prerequisites
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+
GPUs 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 virtualizes the Kubernetes control plane, running each tenant cluster's control plane as a lightweight pod. Instead of a separate physical cluster per tenant or thin namespace isolation, every tenant gets a real, CNCF-certified API server with cluster-admin access. That gives strong control plane isolation per tenant, and with Private Nodes each tenant's workloads run on dedicated bare metal GPU nodes at full GPU performance.
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 GPU hardware?
Private Nodes, the recommended production model, give each tenant dedicated worker nodes with its own CNI and storage, which puts the boundary at the hardware. For internal dev, test and CI/CD by trusted teams, Shared Nodes run on a common node pool. vNode adds workload isolation with Linux user namespaces and seccomp, designed to prevent container breakout and contain blast radius. Hardware network isolation with VLANs, VXLANs and VRFs comes through the Netris integration.
How quickly can an AI cloud provider go live with vCluster?
Boost Run went from decision to production launch in under 45 days for its managed Kubernetes service. That is a customer example rather than a promised implementation time. Your launch depends on machine inventory, driver configuration, networking, storage, templates and service integrations. vMetal and vCluster connect machine orchestration with tenant cluster delivery, reducing the infrastructure workflows you need to build yourself.
Is vCluster production-proven at GPU scale?
vCluster Labs software powers 100K+ GPUs and 1M+ CPUs, serving 50+ GPU clouds & Fortune 500s combined. In production, Lintasarta operates 170+ tenant clusters, while Boost Run completed its managed Kubernetes launch in under 45 days from the decision. These named deployments provide scale and launch examples for teams evaluating the platform for their own infrastructure.
Does vCluster support GPU AI platform tooling like NVIDIA Run:AI or Ray?
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.
Launch Your GPU Cloud Faster
See how vCluster powers GPU cluster management for AI cloud providers at scale.
