ai-cloud
The VMware Replacement for GPU Clouds
Escape VMware's hypervisor tax and high costs. vCluster Platform deploys fully isolated tenant clusters on bare metal, delivering real Kubernetes for every GPU tenant.
Trusted by the fastest-growing AI cloud providers
Problem
Why VMware Falls Short for GPU
Legacy hypervisor architecture was never designed for modern GPU cloud infrastructure.
Hypervisor Tax on GPU Performance
VMware's virtualization layer adds overhead directly to GPU workloads, robbing tenants of the bare metal performance they're paying for.
Isolation Without the Cost
Namespace isolation is too weak. Separate physical clusters per tenant are too expensive. VMware's traditional architecture offers limited practical middle paths for GPU clouds.
Too Slow to Compete
Building a managed GPU platform on VMware takes months of engineering work your competitors are not waiting for.
Solution
A True VMware Replacement for GPU Infrastructure
vCluster Platform virtualizes the Kubernetes control plane itself, running CNCF-certified tenant clusters as lightweight pods on bare metal. Every tenant gets a real API server, etcd, and RBAC with zero hypervisor overhead. Proven across 100K+ GPU nodes and 50+ GPU clouds.
Built for GPU Clouds, Not Legacy VMs
Every layer of the stack is purpose-built to replace VMware on GPU infrastructure, from bare metal provisioning to tenant isolation.
Bare Metal
Zero-Touch GPU Server Provisioning
vMetal handles PXE boot, OS installation, machine registration, and full GPU server lifecycle management. Go from rack to production-ready Kubernetes without manual steps or VMware dependencies.
- PXE boot and OS install automated
- Full GPU server lifecycle managed
- Ships alongside vCluster Standalone: the CNCF-certified control plane that runs as a single binary on bare metal Linux.
Tenant Isolation
Tenant Clusters Without Hypervisor Overhead
Each tenant receives a fully isolated Kubernetes control plane running as a lightweight pod. Spin up hundreds of tenant clusters on shared GPU hardware in seconds, completely replacing VMware's VM-per-tenant model. For production deployments, Private Nodes, dedicated worker nodes with per-tenant CNI and storage, deliver hardware-level isolation.
- Own API server, etcd, and scheduler
- Spins up in seconds, not minutes
- No VM overhead on GPU nodes
Workload Security
Kernel-Native Isolation, No Hypervisor
vNode provides kernel-native workload isolation using seccomp, cgroups, namespaces, and AppArmor, delivering container breakout protection at bare metal GPU speed, with no hypervisor tax.
- No VM overhead on GPU workloads
- Container breakout protection built in
- Bare metal GPU performance preserved
Private Nodes
Private Nodes Per Tenant (Production Default)
Assign Private Nodes, dedicated physical GPU nodes, to tenants to eliminate noisy-neighbor contention. Each tenant's workloads run on reserved hardware with consistent bare metal GPU performance, replacing VMware's resource pooling model. Private Nodes are the production default for GPU cloud operators requiring hardware-level tenant separation.
- No GPU contention between tenants
- Consistent bare metal performance
- Hardware-level tenant separation
AI Environments
Pre-Validated AI Platforms on Tenant Clusters
Certified Stacks turn a bare Kubernetes tenant cluster into a production AI platform in minutes. Partner integrations like Run:AI, Ray, and Jupyter are certified against vCluster tenant isolation.
- Run:AI, Ray, Jupyter as partner integrations
- Cluster to AI platform in minutes
- Certified against 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 a VMware replacement for GPU infrastructure?
vCluster replaces VMware by virtualizing the Kubernetes control plane itself rather than virtualizing hardware. Each tenant gets a fully isolated, CNCF-certified Kubernetes cluster running as a lightweight pod directly on bare metal GPU servers. This eliminates the hypervisor layer entirely, removing VM overhead from GPU workloads and delivering bare metal performance to every tenant. The full stack covers bare metal provisioning via vMetal, tenant cluster orchestration via vCluster Platform, and workload isolation via vNode.
Does eliminating VMware affect tenant isolation on GPU hardware?
No. vCluster delivers stronger, more flexible isolation than VMware without the hypervisor tax. Tenants receive their own Kubernetes API server, etcd, RBAC, and CRDs. For GPU workloads requiring hardware separation, Private Nodes, dedicated worker nodes with per-tenant CNI and storage, are the production default and ensure hardware-level isolation with no cross-tenant resource contention. vNode adds workload isolation at the process level using seccomp, cgroups, and AppArmor, preventing container breakout and limiting blast radius, all without VMs.
How long does it take to replace VMware with vCluster on GPU hardware?
Migration timelines depend on your existing infrastructure, but vCluster is designed to accelerate deployment significantly. Boost Run launched a managed Kubernetes service in less than 45 days. Lintasarta launched a GPU cloud in Indonesia in 90 days using vCluster. The integrated stack from bare metal provisioning through tenant cluster orchestration eliminates the need to assemble and validate separate tools.
Can vCluster handle multiple GPU tenants on shared bare metal hardware?
Yes. vCluster Platform is specifically designed for this. Private Nodes are the production default: dedicated worker nodes with hardware-level separation, their own CNI and CSI, and no cross-tenant workload exposure. Dedicated nodes with reserved GPU hardware are also available, and shared nodes with resource quota boundaries can serve dev, test, CI/CD, and trusted-team workloads at lower cost. This flexible isolation spectrum lets GPU cloud operators match infrastructure costs to tenant requirements without reverting to VMware's VM-per-tenant model.
Is vCluster compatible with NVIDIA GPU infrastructure and AI platforms?
Yes. vCluster is named in the NVIDIA DGX SuperPOD reference architecture. Certified Stacks include partner integrations like Run:AI, Ray, and Jupyter, which are certified to work within vCluster tenant isolation. This means GPU cloud operators can offer production AI environments to tenants without custom integration work on top of bare metal NVIDIA hardware.
What happens to my existing Kubernetes tooling when I replace VMware with vCluster?
vCluster tenant clusters are CNCF-certified Kubernetes control planes with 100% API compatibility. Tenants can install their own CRDs, configure RBAC, and use any standard Kubernetes tooling. GitOps and IaC workflows via Terraform and Argo CD are supported. Operators retain full fleet management through the central UI, CLI, and API, so existing Kubernetes investments are preserved, not replaced.
Replace VMware on Your GPU Infrastructure
See how GPU clouds eliminate hypervisor overhead and launch tenant clusters on bare metal.
