vCluster delivers self-service environments, isolated clusters, and cloud-native tooling directly on your on-prem GPU fleet, without multiplying clusters or infrastructure overhead.
Enterprises invest millions in GPU infrastructure and then watch it underperform because the platform experience doesn’t match what teams actually need. AI teams wait weeks for access. GPUs sit idle. Platform teams become the bottleneck. Data can’t leave the building. The problem isn’t the hardware; it’s the lack of a real platform on top of it.
Capacity must reach AI teams quickly and be used efficiently.
GPU workloads must be planned and allocated efficiently.
Must support OSS and proprietary models across training and inference.
Sensitive data and proprietary models cannot leave the enterprise.
AWS spent a decade building EKS. You don’t have that time, and you shouldn’t need it.
Enterprises try three categories of solutions, and all fall short in different ways when teams grow and GPU fleets expand.
Requires stitching Kubernetes, custom tooling, and homegrown isolation together. Most teams are still building two years in.
Heavy enterprise platforms designed for traditional apps. Not architected for AI workloads, bare metal, or GPU-native multi-tenancy.
Limited real-world deployments and production track record. You don’t want to be the reference customer that proves their tech works.
Trusted by leading AI cloud providers, powering 100K+ GPUs.
vCluster delivers the complete infrastructure stack for enterprise AI factories, from bare metal GPU provisioning up through isolated team environments and ready-to-run AI/ML application stacks. Each layer is production-proven and works independently or as a unified platform.
From request to running AI environment in minutes
Deploy JupyterHub, Ray, Kubeflow, and other AI platforms with production-ready defaults. AI teams go from onboarding request to live environment in minutes, not weeks.
Consistent environments, zero configuration drift
Every team gets the same validated environment, policies, and tooling configuration, no manual setup, no snowflake clusters, no "it works on my machine."
The cloud experience on your own infrastructure
ML engineers trained on AWS get the self-service, turnkey platform they expect, running directly on your on-prem GPU data center with no data leaving the enterprise.
No noisy neighbors between teams
Each workload runs in its own secure runtime using kernel-level isolation, seccomp, cgroups, namespaces, and AppArmor. No VMs, no hypervisor tax.
Full GPU performance with strict boundaries
Direct GPU access with near-zero overhead. Teams get bare metal GPU performance with strict security and resource boundaries enforced at the kernel level.
Safe environments for dynamic AI workloads
Designed for dynamic code execution, package installs, and root access, safely. Built for the realities of training runs, inference services, and agentic workloads.
Isolated Kubernetes per team without cluster sprawl
Each team gets their own fully isolated control plane, their own API server, etcd, and RBAC, on shared GPU infrastructure. No new physical clusters, no new overhead.
Maximize GPU utilization across the entire fleet
Run hundreds of isolated virtual clusters on a single host cluster. Allocate nodes dynamically based on demand. Utilization jumps from 10–30% to 60–90%.
Self-service without IT tickets
Teams provision their own environments in seconds via API, CI/CD, or self-service portal. Platform teams set the guardrails once, then get out of the way.
Zero-touch bare metal GPU provisioning
PXE boot and configure GPU servers automatically. New hardware joins your fleet without manual intervention, from first rack to running workloads in minutes.
Full machine lifecycle management
Provision, upgrade, repurpose, and decommission hardware from one platform. Declarative infrastructure management, IaC, and GitOps-ready from day one.
Hard network isolation per team
Powered by Netris: hardware-enforced multi-tenancy with programmatic VLANs, VRFs, and ACLs provisioned across your full fabric. Data stays exactly where it belongs.
From faster AI delivery to lower infrastructure costs, and the impact of operating like a hyperscaler shows up everywhere.
ML engineers stop waiting on IT tickets and start training models. Self-service environments mean AI projects kick off in hours, not weeks.
Automatic allocation of nodes to teams based on capacity needs drives utilization from 10–30% to 60–90%. Your CapEx investment finally works as hard as it should.
Multi-tenant isolation replaces one-cluster-per-team models. More teams, less hardware. More workloads per GPU, without the ops complexity to match.
Sensitive data and proprietary models stay inside your perimeter, always. Every team environment is isolated at the network and kernel level, with full audit trails.
Built-in observability, cluster updates, backup and DR, compliance, and config management across every team environment, without a sprawl of clusters to manage.
Your platform engineers stop playing scheduler referee and start building leverage. Set the policies, define the guardrails, then let teams self-serve within them.
“With vCluster on DGX systems, you can bring the elasticity, automation, and multi-tenancy of Kubernetes onto your on-prem infrastructure. Get the experience of the public cloud on your DGX systems.”
If you’re building enterprise AI infrastructure, you’re in good company. The same platform powering the world’s fastest-growing AI cloud providers is available for your internal AI factory.
Days from decision to production launch
Virtual clusters
in production
GPUs planned for AI supercluster infrastructure
“vCluster is the first proven solution for operationalizing virtual Kubernetes clusters at scale and we continue to be impressed by the vCluster team and the innovations they ship to customers like us.”
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Scale AI factory infrastructure without frontier lab budgets or a massive platform team.
A blueprint for bringing cloud-grade elasticity and automation to NVIDIA DGX systems.
vCluster and Netris integrate Kubernetes and network automation.
Give every AI team the access they need, without multiplying your infrastructure or your platform team.
