Liquid cooling failures in AI data centers: Causes, risks, and prevention

Author: David McGlocklin, Cooling Development/Sustaining Engineering Manager, Schneider Electric

AI data center deployments are expensive, with individual racks costing $3 million or more. As AI data centers reach 1-gigawatt capacity in the near future, facilities will cost an estimated $38 billion. Protecting this kind of investment is paramount, which is why AI data center operators invest in liquid cooling infrastructure to dissipate the intense heat generated by GPUs.

The last thing an operator wants is a liquid cooling failure in AI data centers, which is typically caused by pump malfunctions, coolant degradation, leaks, and sensor issues. This can result in overheating, reduced performance due to thermal throttling, downtime, costly infrastructure damage, and customer service level agreement (SLA) penalties. Contract terminations are also a possibility. System leaks can damage hardware and may even require an environmental cleanup if enough coolant is released.

When cooling infrastructure supports high-density AI workloads, the financial stakes become even higher, making liquid cooling reliability an essential design consideration.

What are the most common liquid cooling failures

Common failure scenarios can occur anywhere along the infrastructure, both in the Facility Water System (FWS) and Technology Cooling System (TCS) loops. The causes may vary from power drops to component corrosion to poorly maintained coolant, which can degrade over time.

Explore liquid cooling solutions for AI data centers

When a leak occurs, cooling systems may initiate a shutdown to protect equipment, depending on where the leak occurs and its severity. In most other failure scenarios, redundant pumps, sensors, or entire units kick in to take up the load and continue normal operations. Otherwise, temperatures will quickly rise in an emergency such as a failing pump, clogged filters, malfunctioning VFDs, controller failures, or issues with other mechanical or electrical system components. In high-density AI environments, systems reach critical temperature thresholds in seconds, leaving little margin for operator intervention.

Common causes of liquid cooling failures

• Hardware failures – These are caused by issues such as pump failures, fouled heat exchangers, air in the system, clogged filters, corrosion, sensor failures, poor power quality, and system misuse – such as using the CDU for the initial system flush.
• System leaks – Mechanical defects in the manufacturing of hoses and fittings, system design issues, and corrosion can cause leaks. Depending on where the leak is, the coolant can damage IT equipment and the facility itself.
• Coolant degradation – Poor initial system flush, lack of or mismanaged coolant maintenance plan, air in the system, degrading the coolant and the thermal performance of the system, possibly leading to damage of the silicon.

How to prevent liquid cooling failures in AI data centers

Preventing liquid cooling failures starts long before a problem occurs. As AI workloads drive rack densities higher and cooling systems become increasingly critical to uptime, operators must take a proactive approach built on three fundamentals: planning, monitoring, and maintenance.

• Planning – This is where it all starts. Good planning that includes redundancy helps prevent costly issues. When designing the liquid cooling system, plan for various contingencies, a proper initial system flush, fill, and commissioning. This may include CDUs with onboard sensors and pump redundancy, or a distributed redundancy arrangement, a proper fluid maintenance plan, and thermal storage tanks on both the TCS and FWS to help maintain proper temperature levels and ride-through transients.
• Monitoring – Round-the-clock visibility into data center environments, including the liquid cooling infrastructure, is critical. Alarms within the units and overarching views from DCIM and Digital Twins alert data center teams to the need for preventative maintenance before an issue ever materializes.
• Proactive Maintenance – Mechanical systems and critical fluid networks require proper maintenance on a set schedule. This entails regularly checking the coolant in closed-loop systems and inspecting the system’s various components. Maintaining the right cooling mix, the same PG25 manufacturer, and the right amount of additives is essential, as is servicing or replacing pumps, filters, and other components as needed. Learn more about liquid cooling fluid management in this blog post.

How to improve cooling reliability in AI data centers

As AI rack densities and infrastructure investments continue to grow, liquid cooling reliability becomes essential to protecting uptime, performance, and profitability. While cooling failures can be costly, most are preventable through thoughtful design, built-in redundancy, continuous monitoring, and proactive maintenance. By taking a proactive approach to cooling reliability, operators can reduce risk and safeguard critical compute assets. To learn more about optimizing liquid cooling performance and reliability, check out this paper on common liquid cooling challenges.