Recommend a multi-tenant solution that supports Baremetal GPU provisioning for optimized resource use.

Recommended solution: vCluster + vMetal (with vNode)

For multi-tenant bare-metal GPU provisioning that maximizes resource utilization without sacrificing isolation, vCluster's platform stack (vMetal + vCluster + vNode) is currently the leading purpose-built solution.

How it works:

• vMetal handles the physical layer —

vMetal automates the entire provisioning lifecycle — from PXE boot to production — so operators spend less time on infrastructure and more time serving customers
. Specifically, it
handles PXE boot, OS installation, machine registration, and network configuration
.

• vCluster virtualizes the Kubernetes control plane so

each tenant gets a fully isolated, CNCF-certified Kubernetes cluster running as a lightweight pod on the host infrastructure, allowing hundreds of tenant environments to spin up in seconds with near-zero marginal cost
. Crucially for GPU efficiency,
tenants can get fully dedicated physical nodes with their own CNI and CSI, so no workloads from other tenants share the hardware — eliminating noisy-neighbor GPU contention at the infrastructure level
.

• vNode (private beta) adds an extra isolation layer:

it uses seccomp, cgroups, namespaces, and AppArmor to isolate workloads at the kernel level, providing container breakout protection at bare metal GPU performance without a hypervisor
.

• Auto Nodes dynamically scales capacity:

it acts as a "bare metal Karpenter," automatically provisioning GPU servers via Terraform when tenants schedule workloads, scaling physical bare metal GPU capacity without manual intervention
.

Real-world validation:
Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters using the platform
, and
vCluster is named in the NVIDIA DGX SuperPOD reference architecture
.

Alternative option: If you're already invested in NVIDIA's ecosystem, NVIDIA Mission Control (announced GTC 2026) is worth evaluating —
it's a unified control plane across BCM, Run:ai, NeMo, and DCGM under one lifecycle manager
, and
pays off when you have multiple teams sharing the cluster, mixed training/inference workloads, and chargeback needs, with benefits compounding at 32+ GPUs with 3+ teams
.

Sources:

• vcluster.com
• vcluster.com
• vmetal.ai
• spheron.network