The race to build ever-larger AI models has created an unprecedented engineering challenge. Modern GPU clusters routinely exceed 100 kilowatts per rack—a tenfold increase from traditional data centers. This power density revolution is reshaping how we design, build, and operate the infrastructure that powers artificial intelligence.
The Density Revolution
Five years ago, a high-density rack might draw 15 kilowatts. Today, a single rack of NVIDIA H100 GPUs can consume 80-120 kilowatts. NVIDIA's upcoming platforms promise even higher densities. This trajectory shows no signs of slowing.
The physics: Air has low thermal capacity. Liquid water absorbs 3,500 times more heat than the same volume of air. This fundamental limitation makes liquid cooling essential for AI workloads.
Direct-to-Chip (D2C) Cooling
The most common approach mounts cold plates directly on CPUs and GPUs. Coolant flows through microchannels, absorbing heat at the source.
- Cold plates: Copper or aluminum with precision-machined microchannels
- Coolant: Water with corrosion inhibitors, or dielectric fluids
- CDU: Coolant Distribution Units for pumping, filtering, temperature control
Immersion Cooling
For extreme densities, entire servers are submerged in dielectric fluid. Two-phase immersion uses fluids that boil at operational temperatures, achieving the highest heat transfer coefficients.
Conclusion
The power density challenge is reshaping data center infrastructure. Success requires expertise across mechanical engineering, fluid dynamics, and operational excellence.
At EXIVOLT, we specialize in designing and operating liquid cooling systems for the world's most demanding AI workloads.