Liquid cooling has become a critical enabler for modern AI data centers as facilities scale to handle high-density workloads, such as artificial intelligence (AI) and machine learning. As AI workloads drive higher heat densities, the liquid cooling market is projected to expand rapidly—with forecasts projecting 30%+ annual growth through the latter half of this decade. Beyond enabling higher densities, liquid cooling improves thermal efficiency, lowers operational costs, and enhances energy efficiency. Implementation requires specialized equipment such as Coolant Distribution Units (CDUs), cold plates, in-rack manifolds, and rear door heat exchangers (RDHx).
This blog post breaks down the practical considerations for deploying liquid-cooled servers in AI data centers, including:
- Use cases for liquid cooling for AI workloads
- Key infrastructure components for liquid-cooled servers
- Deployment best practices to avoid performance and reliability risks
- How liquid cooling supports sustainability and ESG goals
How to design a data center for liquid cooling: Key requirements, scalability and AI readiness
Phase 1: Design considerations
Start with a comprehensive evaluation of data center design requirements for liquid cooling, taking into account infrastructure and future workload demands. For high-performance applications like AI and HPC, you may find that a direct-to-chip liquid cooling architecture provides the best approach.
- Essential technologies like Coolant Distribution Units (CDUs) regulate the coolant’s temperature and flow, ensuring efficiency across single-phase systems.
- In-rack manifolds distribute the coolant to each cold plate, providing leak-proof connections for easy maintenance.
- Cold plates, mounted directly on CPUs and GPUs, draw heat away from components more effectively than traditional methods, making them critical for high-power density workloads.
- Engaging design partners early in liquid cooling data center design ensures component alignment with operational needs. Factor in initial capital costs and scalability to avoid expensive retrofits later.
Explore AI-ready liquid cooling resources
Liquid cooling deployment strategies: Ensuring compatibility and safety
Phase 2: Deployment strategy
A seamless deployment strategy requires close collaboration among stakeholders. Data center operators, system integrators, and cooling vendors should work together on tasks such as coolant distribution unit (CDU) design and implementation of liquid cooling infrastructure for high-density AI racks. Before deploying liquid cooling for AI and HPC workloads, verify that your facility’s infrastructure can accommodate CDUs, cold plates, and rear door heat exchangers. These components dissipate heat from the coolant before it is recycled.
Testing for energy efficiency gains, such as reductions in Power Usage Effectiveness (PUE), is essential to validate performance improvements. Safety protocols like leak detection systems and quick-disconnect in-rack manifolds should be in place to mitigate operational risks. Thorough performance testing and with detailed documentation ensure your system is energy-efficient and secure.
Maintaining liquid-cooled data centers: Best practices for longevity and efficiency
Phase 3: Maintenance essentials
Effective maintenance is crucial for the long-term success of liquid cooling for AI and HPC workloads. Regularly inspect key components such as CDUs, in-rack manifolds, and cold plates to prevent leaks, blockages, or system failures. Redundancy in liquid cooling infrastructure prevents interruptions to operations, even in case of component failure.
Rear door heat exchangers also require maintenance to ensure consistent heat dissipation and coolant recycling. Ongoing training for maintenance teams and strong vendor partnerships help address issues quickly. Routine evaluations help optimize system performance and allow for timely upgrades to maintain energy efficiency and sustainability.
Maximizing data center efficiency and sustainability with liquid cooling
According to Accenture, carbon emissions for AI may account for 3.4% of total global emissions. Addressing sustainability goals should still be front and center with liquid cooling representing a key advancement in enabling energy-efficient and sustainable data center operations. By focusing on design, deployment, and proactive maintenance, operators can harness the full potential of liquid cooling systems and specialized components like CDUs, cold plates, in-rack manifolds, and rear door heat exchangers.
These best practices help maintain peak operational efficiency and extend the lifespan of critical infrastructure. This approach to liquid cooling also supports ESG and data center sustainability goals to ensure an environmentally responsible operation.
Resources for deploying liquid-cooled data centers
By approaching liquid cooling as an integrated system and focusing on thoughtful design, coordinated deployment, and proactive maintenance, data center operators can unlock higher densities, improve efficiency, and advance sustainability goals without compromising reliability. To learn more about how to plan, deploy, and scale liquid cooling for AI-ready data centers, explore our liquid cooling resources.
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