AI/ML Infrastructure Planning Guide

The AI/ML Infrastructure Challenge

Artificial intelligence and machine learning workloads are reshaping data center infrastructure requirements at a pace that outstrips traditional capacity planning assumptions. A single GPU training cluster can consume 40-80 kW per rack, compared to 8-15 kW for conventional enterprise compute. This 5-10x density increase cascades into every infrastructure domain: electrical distribution must handle higher per-circuit loads, cooling systems must reject significantly more heat per square foot, and structural systems must support heavier equipment in tighter footprints.

The challenge is not simply scaling up existing designs. AI/ML infrastructure requires a different topology where power and cooling are co-engineered around GPU cluster geometry rather than distributed uniformly across a raised floor. GridCore addresses this by treating AI/ML deployments as a distinct configuration profile within each deployment model.

Power Distribution for GPU Clusters

Electrical Topology Adjustments

Traditional data center power distribution assumes relatively uniform load distribution across rows and zones. AI/ML deployments concentrate load in GPU cluster blocks that may represent 60-80% of the total facility IT load in 20-30% of the floor space. This concentration demands dedicated electrical feeds from the medium-voltage distribution to the GPU cluster zones, bypassing the general-purpose low-voltage distribution.

GridCore configurations for AI/ML workloads route dedicated transformer and switchgear capacity to GPU zones with independent metering and protection coordination. Each GPU row receives its own power distribution unit (PDU) with per-circuit monitoring at a granularity of individual server shelves, enabling real-time load balancing and predictive maintenance.

UPS and Backup Power Considerations

GPU training workloads have a complex relationship with UPS protection. While the GPU hardware itself is valuable and benefits from power continuity, training jobs can often checkpoint and resume, meaning a brief power interruption may not require full ride-through. Many AI/ML operators choose to protect only the storage tier (holding training data and checkpoints) with full UPS coverage while accepting generator-only backup for the GPU compute tier, significantly reducing UPS capital cost.

Thermal Management for High-Density Compute

Direct Liquid Cooling

At rack densities above 30 kW, air cooling becomes impractical as the sole thermal management strategy. Direct liquid cooling (DLC) uses a coolant loop that makes physical contact with GPU heat sinks, removing heat at the source before it enters the room air. This approach is 10-20x more efficient per watt of heat removed compared to air-based methods and enables rack densities up to 100+ kW.

GridCore supports DLC integration across all deployment models. Container-based deployments pre-plumb coolant loops within the container envelope. Modular buildings include manifold systems at module boundaries for DLC distribution. Building + skid deployments centralize the cooling plant on mechanical skids with distribution headers running to GPU zones within the building.

Rear-Door Heat Exchangers

For mixed-density environments where some racks operate at AI/ML densities while others run conventional loads, rear-door heat exchangers (RDHx) offer a hybrid approach. RDHx units mount on the back of individual racks and use chilled water to absorb exhaust heat before it enters the hot aisle, allowing high-density racks to coexist with standard air-cooled infrastructure.

Facility Design Implications

Structural Load Planning

GPU servers are significantly heavier than standard 1U/2U servers. A fully populated AI/ML rack can weigh 2,500-4,000 lbs, compared to 1,500-2,000 lbs for a conventional compute rack. Floor loading capacity must be validated for these concentrated point loads, particularly in modular and container deployments where structural capacity may be more constrained than in purpose-built buildings.

Network Fabric Considerations

AI/ML training clusters require high-bandwidth, low-latency interconnects between GPU nodes. InfiniBand or high-speed Ethernet (400G/800G) fabric typically requires dedicated fiber pathways, leaf-spine switch placement within or adjacent to the GPU zone, and cable management systems designed for the higher density and bend-radius requirements of high-speed optics.

Deployment Model Selection for AI/ML

Each GridCore deployment model supports AI/ML configurations, but the optimal choice depends on scale, timeline, and site constraints:

• Container-based: Ideal for edge AI inference, small training clusters (1-4 MW), or rapid deployment scenarios. Pre-integrated DLC and power distribution within the container envelope enable 12-16 week delivery for standardized GPU configurations.
• Modular building: Best for mid-scale AI/ML programs (2-10 MW) that need phased growth. Modules can be configured as dedicated GPU halls with DLC infrastructure and expanded by adding additional modules as GPU capacity demands grow.
• Building + skid: Optimal for large-scale AI training campuses (10+ MW) where centralized cooling plants and bulk power distribution deliver the best economics. Skid-based mechanical systems can include dedicated DLC plants sized for the full GPU program.