Nice Karman

A predictable development in the AI world: managing heat at data centers

Any electrical engineer saw this one coming: how to manage heat at data centers, where massive rows of servers with heavy-duty wiring require around-the-clock power. With that in mind, Los Angeles company Karman Industries recently announced the launch of what it calls the "Heat Processing Unit (HPU)," a modular 10MW integrated thermal platform engineered to solve the "speed-to-power" crisis facing AI hyperscalers.

By consolidating massive heat management infrastructure into high-density modular packages, Karman's HPU concept is based on "[unlocking] rapid deployments while eliminating water consumption," the company explained in a January 15th press release. "HPUs optimize energy consumption, providing the most efficient cooling while unlocking heat reuse for power generation or district heating."

The "rise" of the HPU, as Karman puts it, is being driven by a shift from cooling to processing. As AI clusters scale toward multi-gigawatt capacity, utilizing the latest architectures from chip OEMs like Nvidia, heat is no longer only a bottleneck but also an asset to leverage.

Traditional designs require sprawling mechanical yards filled with 500+ disparate chillers and dry coolers, necessitating miles of complex piping and months of on-site assembly. With such designs, data centers use energy to remove heat--an almost Shakespearean paradox. Karman has been "working in the background for 18 months," it claims, to create the HPU that efficiently manages and utilizes this heat.

“In the race to stand up AI capacity, time is the most expensive variable,” said David Tearse, CEO and Co-Founder of Karman Industries. “We’ve moved beyond the era of legacy chillers to HPUs. By shrinking the footprint of the mechanical yard by 80%, we don’t just save land; we eliminate the ‘snowball effect’ of infrastructure complexity, allowing hyperscalers to move from 'shovels in the ground' to 'chips in the rack' many months faster while unlocking additional compute.”