Generative Design for Direct-to-Chip Liquid Cooling for Data Centers

Rapid growth in artificial intelligence workloads is driving up data center power densities and increasing the need for advanced thermal management. Direct-to-chip liquid cooling can remove heat efficiently at the source, but many cold-plate channel layouts remain heuristic and are not optimized for the strongly non-uniform temperature distribution of modern heterogeneous packages. This work presents a generative design framework for synthesizing cooling channel geometries for the NVIDIA GB200 Grace Blackwell Superchip. A physics-based finite-difference thermal model provides rapid steady-state temperature predictions and supplies spatial thermal feedback to a constrained reaction-diffusion process that generates novel channel topologies while enforcing inlet, outlet, and component constraints. Compared with a baseline parallel channel design, the resulting channels achieve more than a 5 °C reduction in average temperature and over 35 °C reduction in maximum temperature.