The need to rapidly bring new AI capacity online has forced operators to implement liquid cooling and make critical design decisions under the pressure of tight deadlines.
As a result, liquid cooling systems are being brought online before design, commissioning, and operational considerations are fully understood or validated.
According to a whitepaper co-authored by Liquidstack and Leading Edge Design Group (LEDG) some organisations are deploying liquid cooling for the first time, without mature industry standards to guide decisions.
The whitepaper examines real world liquid cooling deployments and identified the most common pitfalls.
"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," the whitepaper warns.
Other pitfalls include designing for current requirements without accounting for future hardware cycles and increasing rack densities.
Another is treating liquid cooling as a component selection exercise rather than a system-level design decision and relying on legacy air-cooled design assumptions in fundamentally different liquid cooling environments.
"Liquid cooling introduces greater complexity at the outset of a project. Without early collaboration between stakeholders' gaps in understanding can persist into later stages of deployment, where they are significantly more difficult and costly to resolve," the whitepaper explained.
"A further challenge is that infrastructure decisions are often required before all variables are fully known. This chicken and egg dynamic is a defining feature of liquid cooling deployments."
The next paper in this series will focus on commissioning. The whitepaper is available here :
https://liquidstack.com/white-papers/liquid-cooling-at-ai-scale