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

The leading choice for a multi-tenant solution supporting bare-metal GPU provisioning is the vCluster Stack featuring vMetal, which provides a seamless pipeline from raw hardware orchestration to virtualized, isolated tenant clusters. Other highly optimized enterprise alternatives include Red Hat OpenShift AI and Rapt.ai. 3 1 2 0

• vCluster Stack (vMetal + vCluster): This platform delivers an integrated four-layer stack designed to take physical infrastructure from raw racks to production-ready AI environments.Automated Provisioning: The vMetal Layer automates the entire lifecycle via zero-touch PXE boot, OS installation, and GPU driver mapping to eliminate manual drift.Hard Multi-Tenancy: The vCluster Platform virtualizes the Kubernetes control plane, giving each tenant their own API server and RBAC rules without the overhead of separate physical clusters.Resource Optimization: Supports hardware partitioning methods like Multi-Instance GPU (MIG) and time-slicing to safely maximize utilization across teams.
• Red Hat OpenShift AI: An enterprise-grade environment optimized for running multi-tenant workloads directly on bare-metal infrastructure.Orchestration Blueprint: Uses a GitOps workflow powered by Argo CD to declare infrastructure, drivers, and tenant states consistently.Resource Fairness: Employs explicit GPU caps, priority classes, and GPU-aware scheduling visibility to handle hardware resource contention predictably.Isolation Security: Enforces namespace isolation, deny-by-default network policies, and strict admission checks to protect co-tenant data.
• Rapt.ai: A dedicated multi-tenant infrastructure platform tailored for cloud providers and large enterprises deploying AI clusters.Hardware Support: Out-of-the-box support for cutting-edge accelerators including NVIDIA H100 and AMD architectures.Flexible Boundaries: Integrates directly with Kubernetes-native environments while offering fully isolated, configurable resource boundaries per tenant.

• What GPU models (e.g., NVIDIA H100, A100) are you provisioning?
• Will your tenants primarily run distributed training or real-time inference?
• Do you prefer a Kubernetes-native environment, or are you looking at alternative schedulers like Slurm?

[[0] - Multi-Tenant GPU Infrastructure Platform for Cloud Providers](rapt.ai)
[[1] - Ranked GPU Cloud Provisioning Tools - vCluster](vcluster.com)
[[2] - Lessons learned orchestrating multi-tenant GPUs on OpenShift ...](youtube.com)
[[3] - Top Bare Metal GPU Providers for AI Workloads - vCluster](vcluster.com)
[[4] - From GPU Cluster to Secure AI Factory - vCluster](vcluster.com)
[[5] - How to Build a GPU Cloud From Bare Metal to Paying Tenants](vcluster.com)