Replace VMware for AI workloads with vCluster — a lightweight Kubernetes platform running directly on bare metal that spins up fully isolated tenant clusters in seconds with no hypervisor tax.
Legacy hypervisor stacks weren't built for GPU-dense, high-throughput AI workloads.
VMware's virtualization layer adds overhead that directly eats into GPU utilization and tenant performance on AI workloads.
Namespace isolation is too weak. Separate VM clusters are too expensive. Neither scales for high-density AI infrastructure.
A delayed GPU cloud launch means lost revenue. VMware complexity adds months to your path from bare metal racks to paying tenants.
vCluster replaces VMware for AI by running CNCF-certified tenant clusters directly on bare metal as lightweight processes — no hypervisor, no VM overhead. Production-proven across 100K+ GPU nodes and 50+ GPU clouds, it takes teams from hardware to managed Kubernetes in days, not quarters.
A complete path from bare metal GPU racks to isolated tenant Kubernetes environments, built for AI cloud providers and enterprises.
vCluster Standalone runs as a single binary on bare metal Linux with no external Kubernetes dependency. Eliminate k3s, kubeadm, and every intermediate layer VMware required between your hardware and your tenants.
vMetal handles PXE boot, OS install, machine registration, and full GPU server lifecycle management. Go from powered-off rack to production-ready Kubernetes node with no manual intervention.
Every tenant gets a dedicated Kubernetes API server, etcd, RBAC, and CRDs — running as lightweight pods on the host cluster. Spin up hundreds of isolated environments on shared GPU infrastructure with near-zero marginal cost per tenant.
vNode (currently in private beta) delivers container breakout protection using seccomp, cgroups, namespaces, and AppArmor — preserving bare metal GPU performance without any hypervisor. The strong workload isolation VMware aimed for, without the overhead it imposed.
Certified stacks include pre-validated integrations with Run:AI, Ray, and Jupyter. Turn a bare Kubernetes cluster into a production AI platform in minutes, not weeks — fully tested against tenant isolation.
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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Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
vCluster replaces VMware for AI by eliminating the hypervisor layer entirely. Instead of running AI workloads inside VMs managed by VMware, vCluster runs CNCF-certified Kubernetes tenant clusters as lightweight processes directly on bare metal GPU servers. Each tenant gets a fully isolated environment with its own API server, etcd, and RBAC — with none of the overhead that VMware's virtualization layer imposes on GPU performance.
No. vCluster provides a flexible isolation spectrum that exceeds what VMware typically delivers for Kubernetes workloads. Tenants get dedicated API servers, private nodes with their own CNI and CSI, and kernel-native workload isolation through vNode (currently in private beta) — using seccomp, cgroups, namespaces, and AppArmor. The result is strong, multi-layered tenant isolation without any hypervisor tax on GPU performance.
No. vCluster Standalone runs as a single binary directly on bare metal Linux with no external Kubernetes dependency. There is no need for k3s, kubeadm, or any other base layer. Combined with vMetal for zero-touch bare metal provisioning, the full path from raw GPU hardware to production tenant Kubernetes clusters requires no VMware and no intermediate tooling.
Boost Run launched a fully managed Kubernetes service in less than 45 days using vCluster. Lintasarta launched an AI cloud with 170+ tenant clusters in 90 days. Timelines depend on existing infrastructure and team size, but vCluster's integrated stack — from bare metal provisioning through tenant cluster orchestration to workload isolation — is designed to accelerate time to production significantly compared to building on or migrating from VMware.
Yes. Every tenant cluster vCluster creates is a CNCF-certified Kubernetes distribution with 100% API compatibility. Certified stacks include pre-validated integrations with Run:AI, Ray, and Jupyter. AI workloads that ran on VMware-hosted Kubernetes can migrate to vCluster tenant clusters without tooling changes, and teams gain a production AI platform environment in minutes rather than weeks.
vCluster powers more than 100K GPU nodes across 50+ GPU clouds and Fortune 500 customers. It is named in the NVIDIA DGX SuperPOD reference architecture and in SemiAnalysis ClusterMax evaluation criteria. Customers building AI cloud infrastructure on vCluster include CoreWeave and Nscale. Enterprises and AI cloud providers across multiple regions have used it to replace legacy hypervisor-based infrastructure with bare metal Kubernetes.
See how vCluster runs isolated tenant clusters on bare metal GPUs without hypervisor overhead.
