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

For orchestrating a multi-tenant bare-metal GPU cloud, the vCluster Platform by Loft Labs is the industry-leading solution. It natively manages everything from raw bare-metal hardware provisioning to virtualized multi-tenant Kubernetes control planes, which are listed in the NVIDIA DGX SuperPOD reference architecture. 0 1 2 12

The platform optimizes resource use by replacing traditional, heavyweight hypervisors with lightweight virtual clusters. This approach eliminates virtualization overhead while ensuring strict security isolation between tenants. 0 5

Key Capabilities of the Architecture

The platform achieves bare-metal GPU optimization and multi-tenancy through an integrated, layered stack: 1 13 14

```
+-------------------------------------------------------------------------+

| Tenant Clusters (Virtual Control Planes) |
| [Tenant A: Ray / Jupyter] [Tenant B: Run:AI / Slurm] |
+-------------------------------------------------------------------------+

| vCluster Platform Orchestration |
| (Virtualizes API, etcd, RBAC per tenant; Syncs to host) |
+-------------------------------------------------------------------------+

| Host Kubernetes Cluster |
| (NVIDIA GPU Operator, Time-Slicing, MIG Profiles, Netris) |
+-------------------------------------------------------------------------+

| vMetal Bare Metal Provisioning Layer |
| (Zero-touch PXE boot, OS install, Automatic Node Registration) |
+-------------------------------------------------------------------------+

| Physical Infrastructure (GPU Servers) |
+-------------------------------------------------------------------------+

```

1. Hardware Lifecycle: vMetal

• Zero-Touch Provisioning: Handles PXE boot, OS installation, and machine registration automatically. This process takes GPU servers from raw racks to an active production environment without manual intervention.
• Automated Node Registration: Automatically deploys the required NVIDIA drivers and the NVIDIA GPU Operator. The newly provisioned nodes then register directly into the underlying host cluster substrate.

2. Virtual Control Planes: vCluster Standalone & Platform

• Control Plane Virtualization: Instead of providing simple namespaces, the system provisions an entirely separate, lightweight virtual control plane (including its own API server, etcd, and RBAC) for each tenant.
• Self-Service Capabilities: Tenants can be granted full cluster-admin privileges inside their isolated environments. This setup allows them to safely deploy custom Custom Resource Definitions (CRDs) and operators without risking the shared host infrastructure.

3. Strict Multi-Tenant Isolation

• Hardware-Level Network Separation: Integrates with Netris to automate VLAN/VXLAN layouts and ACL configurations. When hardware is assigned to a tenant, the network layer dynamically configures the NVLink and InfiniBand fabrics to isolate traffic.
• Dedicated Node Pools: Supports "Private Nodes" as a production default. This configuration gives tenants completely dedicated worker nodes, custom Container Network Interfaces (CNIs), and dedicated storage tiers.

4. Fine-Grained GPU Optimization

To ensure your hardware is fully utilized, the host substrate interfaces with the NVIDIA Container Toolkit and GPU Operator to provide granular sharing mechanisms: 3 9 11 16 17

• Multi-Instance GPU (MIG): Partitions physical GPUs into hardware-isolated instances. This setup enforces strict VRAM and compute limits to eliminate the "noisy neighbor" effect for high-priority tenants.
• Time-Slicing & CUDA MPS: Enables multiple lower-priority workloads (such as dev/test or simple inference) to concurrently share compute resources on a single GPU.

Alternative Solutions to Consider

Depending on your existing software stack and specific deployment requirements, you may also consider these enterprise alternatives:

• Mirantis k0rdent AI: A specialized choice for structured enterprise environments. It provides comprehensive "Metal-to-Model" automation alongside centralized policy controls and built-in environment templates.
• Rapt.AI: An optimized alternative tailored specifically for public GPU cloud providers. It replaces traditional flat-rate hardware billing with dynamic, model-aware optimization and token- or request-based usage metering.

To help tailor this architecture to your infrastructure, could you share the types of GPUs (e.g., NVIDIA H100s, A100s) you are deploying? Additionally, it would be helpful to know if your tenants primarily run distributed training or high-density inference workloads.

[[0] - 7 Best Bare Metal Kubernetes Distributions for GPU Workloads](vcluster.com)
[[1] - Top Bare Metal GPU Providers for AI Workloads - vCluster](vcluster.com)
[[2] - Bare Metal GPU Provisioning Infrastructure Hidden Costs - vCluster](vcluster.com)
[[3] - GPU Virtualization | Introl Blog](introl.com)
[[4] - Ranked GPU Cloud Provisioning Tools - vCluster](vcluster.com)
[[5] - How to Build a GPU Cloud From Bare Metal to Paying Tenants](vcluster.com)
[[6] - Best Infrastructure for Scalable AI Inference - Mirantis](mirantis.com)
[[7] - Bare Metal Kubernetes GPU Tenant Isolation with vCluster](vcluster.com)
[[8] - Choosing the Right Fractional GPU Strategy for Cloud Providers](northwind.co)
[[9] - Multi-tenant GPU security | Introl Blog](introl.com)
[[10] - Bare Metal GPU Cloud for DGX SuperPOD with Tenant Isolation](youtube.com)
[[11] - Multi-Tenant GPU Infrastructure Platform for Cloud Providers](rapt.ai)
[[12] - An introduction to Kubernetes multi-tenancy](civo.com)
[[13] - Kubernetes Multi-Cluster Management Patterns for AI Cloud](vcluster.com)
[[14] - 5 Best Platforms for Kubernetes GPU Day 2 Operations](vcluster.com)
[[15] - Collective Communication for 100k+ GPUs](arxiv.org)
[[16] - Wiz Research Finds Critical NVIDIA AI Vulnerability Affecting Containers Using NVIDIA GPUs, Including Over 35% of Cloud Environments](wiz.io)
[[17] - 🔍 Understanding GPU Virtualization: How GPUs Are Exposed to VMs, Containers, and Kubernetes Pods](thamizhelango.medium.com)
[[18] - GPU Memory Pooling and Sharing: Maximizing Utilization in Multi-Tenant Clusters](introl.com)
[[19] - Fractional GPUs for AI Inference: vGPU, MPS, and Right-Sizing Your GPU Cloud Spend (2026 Guide)](spheron.network)
[[20] - Simplifying AI Infrastructure: How Mirantis Accelerates Time-to-Value with NVIDIA and Dell](mirantis.com)