As enterprises deploy larger models, denser GPU clusters, and always-on inference pipelines, the limiting factor is no longer access to compute alone, it is the ability to sustain that compute at peak performance, predictably and efficiently. This is where data center cooling for AI workloads has emerged as a board-level concern.