Liquid Cooling at AI Scale: Lessons from the Field
How to build a resilient, scalable liquid cooling foundation for high‑density AI infrastructure
Given the pressure to bring new AI capacity online faster than ever, operators are making critical infrastructure decisions under increasingly compressed timelines. As a result, liquid cooling systems are being brought online before design, commissioning, and operational considerations are fully understood or validated. And many organizations are deploying liquid cooling for the first time, without mature industry standards to guide decisions.
Such design shortcuts can lead to higher costs and delays, constrain performance, increase operational risk, and ultimately impact the compute capacity the infrastructure was built to support.
Developed by LiquidStack and Leading Edge Design Group (LEDG) and drawing on real-world deployment experience, this white paper provides a practical framework for designing liquid cooling as an interconnected system from the outlet, aligning the IT load, facility infrastructure, CDU, Technology Cooling System (TCS), and controls.
What you’ll learn:
- The most common design pitfalls emerging across liquid cooling deployments and how to avoid them.
- How redundancy, system segmentation, and “blast radius” should factor into resilient data hall design.
- How to align CDU selection with workload requirements, facility water conditions, and system architecture.
- Key considerations for TCS design, pressure and flow management, monitoring, controls, and operational ownership.
Whether you’re deploying liquid cooling for the first time or scaling high-density AI infrastructure, this paper provides the framework to make better design decisions early, before they become costly operational problems.