AI infrastructure is forcing a fundamental rethink of data center cooling architectures. As GPU power consumption accelerates beyond 1,000W per device and rack densities move toward megawatt-scale deployments, thermal management is no longer simply about removing heat efficiently. Mechanical integration, fluid distribution, orientation flexibility, and infrastructure scalability have become equally critical challenges.
While much of the industry focus has centered on thermal performance alone, conventional cooling technologies increasingly introduce mechanical limitations as infrastructure scales. Higher coolant flow rates, larger manifolds, elevated pump pressures, and complex hydraulic balancing all add operational overhead, physical integration challenges, and deployment complexity. As compute density rises, these constraints become barriers to scalable AI infrastructure.
The limits of conventional cooling
Traditional single-phase liquid cooling relies on sensible heat transfer, where coolant absorbs heat by increasing in temperature as it flows across components. This approach becomes increasingly difficult to scale at extreme thermal loads because greater heat removal requires higher flow rates and more aggressive pumping infrastructure.
