Bare-Metal Orchestration

Administrators define the desired physical server state—including firmware versions, BIOS settings, and disk partitioning—in code. The orchestration platform automatically reconciles the actual hardware state with this declaration, enabling immutable infrastructure patterns for physical nodes. This eliminates manual racking errors and ensures every GPU node is provisioned identically.

• Uses YAML or JSON-based configuration files
• Integrates with Redfish API for out-of-band management
• Supports PXE boot and iPXE for network-based OS installation

By eliminating the virtualization layer, bare-metal orchestration grants workloads direct, unmediated access to GPU accelerators. This avoids the I/O overhead and PCIe passthrough complexity associated with hypervisors, ensuring that NCCL collective operations and GPUDirect RDMA data paths operate at full line rate without translation penalties.

• No virtual GPU (vGPU) licensing costs
• Full Multi-Instance GPU (MIG) partitioning control
• Eliminates the 'noisy neighbor' problem of shared virtualized hosts

The orchestrator manages the entire firmware stack, including GPU vBIOS, NIC firmware, and storage controller firmware, ensuring version consistency across the cluster. It automates the rollout of CUDA drivers and GPU Operator components, performing rolling updates that cordon and drain workloads before re-provisioning nodes to prevent training job interruptions.

• Integrates with DCGM for health validation post-update
• Maintains a cryptographically signed firmware manifest
• Supports canary deployment strategies for driver rollouts

The orchestration layer exposes detailed hardware topology metadata—including NUMA node layout, GPU-to-GPU NVLink connectivity, and network fabric locality—to the scheduler. This enables intelligent placement of distributed training jobs that minimizes inter-node latency and maximizes intra-node bandwidth by aligning pod placement with the underlying physical spine-leaf architecture.

• Considers HBM3e memory capacity per accelerator
• Aligns NCCL ring formation with physical topology
• Prevents cross-NUMA memory access penalties

Orchestration platforms interface with Baseboard Management Controllers (BMCs) via the Redfish API to enforce power capping policies and monitor thermal thresholds. This is critical for direct liquid cooling environments where coolant flow rates and inlet temperatures must be coordinated with compute load to prevent thermal throttling of high-TDP GPUs during sustained training runs.

• Dynamic power capping to stay within rack power budgets
• Automated emergency shutdown on thermal excursion
• Integration with facility-level DCIM systems

Nodes are provisioned from versioned, pre-built OS images containing the exact kernel version, CUDA toolkit, and container runtime required. This ensures bit-for-bit reproducibility across the cluster and eliminates configuration drift. Images are streamed via iSCSI or NVMe-oF from a central repository, allowing a node to transition from bare silicon to a fully operational Kubernetes worker in minutes.

• Supports Lustre client configuration in the image
• Integrates with private container registries for air-gapped sites
• Enables rapid rollback to known-good images

A virtualization technique that assigns a physical GPU directly to a single virtual machine, granting it full and exclusive control of the accelerator. This achieves near-native performance without a hypervisor abstraction layer.

• IOMMU/VT-d: Uses hardware-assisted DMA remapping to isolate the GPU for the VM
• No Driver Interference: The VM loads the native NVIDIA or AMD driver directly onto the physical hardware
• Use Case: Ideal for running isolated AI workloads on shared bare-metal hosts while maintaining performance

GPU passthrough bridges the gap between true bare-metal and virtualized environments when hardware consolidation is required.

A rigorous stress-testing process performed on new GPU hardware before production deployment. Bare-metal orchestration pipelines integrate burn-in testing to identify early component failures.

• Thermal Soak: Runs GPUs at maximum TDP for extended periods to detect cooling deficiencies
• Memory Testing: Validates HBM3e memory integrity under sustained bandwidth saturation
• Fault Remediation: Automatically flags failing units for RMA before they enter the production pool

This practice ensures that only validated hardware enters the AI compute fleet, reducing mid-training failures that waste expensive compute time.