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

To achieve optimized resource use in a multi-tenant bare-metal GPU environment, you need a solution that combines bare-metal orchestration with advanced GPU virtualization and slicing technologies.

➡️ Top Multi-Tenant Bare-Metal GPU Solutions

• NVIDIA AI Enterprise with MCM: NVIDIA's Multi-Instance GPU (MIG) and Multi-Console Management (MCM) allow a single bare-metal GPU to be safely partitioned into up to seven fully isolated instances, ensuring hardware-level multi-tenancy without hypervisor overhead.
• Canonical MAAS with LXD: Metal as a Service (MAAS) provisions the raw physical servers, while LXD provides ultra-lightweight system containers that access the host GPUs at near-native speeds with strict tenant isolation.
• OpenStack Ironic with Nova: This classic open-source cloud architecture uses Ironic to deploy operating systems directly onto bare-metal nodes, while Nova manages the pass-through or slicing of PCIe GPU resources to separate project tenants.

🔎 Key Selection Criteria

When finalizing your architecture, evaluate solutions based on three core technical layers: 7

• 1. Hardware Isolation: Ensure the platform supports technologies like NVIDIA MIG or vGPU to partition physical cards at the silicon level, preventing one tenant's workload from crashing or slowing down another.
• 2. Orchestration Control: Look for robust API-driven provisioning (like MAAS or Ironic) that can rapidly wipe, re-image, and re-allocate bare-metal nodes to different tenants.
• 3. Secure Multi-Tenancy: The control plane must offer strict Role-Based Access Control (RBAC), isolated network namespaces (VLAN/VXLAN), and tenant-specific metric tracking for billing.

To help narrow down the absolute best technical stack or architecture for your infrastructure, could you share a bit more context about your current environment?

[[0] - “GenAI on Prod”:Mastering GPU Efficiency: Optimizing GenAI Workloads on Kubernetes (Part-3)](medium.com)
[[1] - AI Infrastructure Knowledge Base · Yobitel](yobitel.com)
[[2] - NVIDIA Multi-Instance GPU (MIG)](nvidia.com)
[[3] - Setting up a secure shared development environment with LXD](ubuntu.com)
[[4] - How to Use MAAS (Metal as a Service) for Ubuntu Provisioning](oneuptime.com)
[[5] - AI at the edge: simplifying infrastructure with Cisco and Canonical](ubuntu.com)
[[6] - OpenStack Ironic Overview, Benefits & Bare Metal Server Uses](mirantis.com)
[[7] - Building a Production-Grade LLM Application in 8 Weeks: Architecture Decisions, Pitfalls, and Best Practices](dev.to)
[[8] - Getting most out of your GPUs using MIG](rajatpandit.com)
[[9] - Renting bare-metal (as a Service) with MAAS](canonical.com)
[[10] - AI and OpenStack Ironic: Bare Metal Provisioning and Cloud-Native Infrastructure](linkedin.com)
[[11] - Bare Metal Cloud vs Dedicated Servers for AI Workloads](atlantic.net)