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Technical White Paper

Thermal Management for High-Density AI Clusters

July 18, 2024 11 Min Read Clayton Reynar

The Thermal Wall

Modern AI accelerators have pushed power density beyond the practical limits of air cooling. A single NVIDIA H100 GPU draws up to 700W, and a fully loaded DGX H100 system consumes over 10kW. At rack densities approaching 100kW, traditional air-cooled data centers simply cannot remove heat fast enough.

Direct-to-Chip Liquid Cooling

Direct liquid cooling (DLC) delivers coolant directly to the heat-generating components, achieving thermal resistance values 10-100x better than air cooling. Two primary architectures dominate the enterprise landscape:

Cold Plate Systems

Sealed cold plates attach directly to GPUs and CPUs, circulating coolant through micro-channel heat exchangers. This approach integrates with existing rack infrastructure and requires minimal facility modifications.

Immersion Cooling

Single-phase or two-phase immersion submerges entire servers in dielectric fluid. While offering superior thermal performance, immersion requires purpose-built tanks and specialized maintenance procedures.

Efficiency Metrics

The efficiency gains from liquid cooling are substantial:

  • PUE reduction from 1.4-1.6 (air-cooled) to 1.05-1.15 (liquid-cooled)
  • GPU throttling elimination — sustained boost clocks increase training throughput by 10-15%
  • Density improvement — 3-4x more compute per square foot of data center space
  • Heat reuse potential — warm water return enables building heating integration

Deployment Considerations

Transitioning to liquid cooling requires careful planning across multiple disciplines:

  1. Facility assessment — piping infrastructure, water treatment, and leak detection systems
  2. Redundancy design — coolant distribution unit (CDU) N+1 configurations
  3. Maintenance protocols — technician training for wet-side servicing
  4. Monitoring integration — coolant temperature, flow rate, and pressure telemetry

Conclusion

Liquid cooling is no longer optional for AI-scale infrastructure. Organizations planning GPU cluster deployments must factor thermal management into their facility strategy from day one, not as an afterthought. The investment in liquid cooling infrastructure pays for itself through improved performance, reduced energy costs, and future-proofed density headroom.

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