Over the years, artificial intelligence (AI), machine learning, and high-performance computing (HPC) have rapidly transformed data centre design. Beyond the limits of traditional air cooling, compute-intensive workloads continue to push rack power densities, and liquid cooling has become an increasingly important strategy for maintaining performance, energy efficiency, and equipment reliability.
But there is no universal liquid cooling solution for every deployment. Cooling requirements vary even within a hyperscale AI factory, and the needs of a GPU cluster differ. The ideal cooling architecture depends on factors such as workload intensity, rack density, available infrastructure, scalability, and operational costs. Understanding the strengths of multiple liquid cooling technologies helps organisations choose solutions that support both current performance requirements and future growth.
Why Is Liquid Cooling Becoming Essential for AI Workloads?
High-performance CPUs and multiple GPUs in modern AI servers generate more extreme heat than conventional enterprise servers. As rack densities increase, traditional air cooling struggles to dissipate the heat and even consumes excessive energy, requiring solutions such as liquid cooling.
Here are some reasons that make liquid cooling an attractive option for organisations modernising their data centre infrastructure:
- Liquids absorb heat far better than air, keeping hardware within safe operating limits and reducing lifespan degradation and failures.
- While traditional air conditioning consumes significant energy, updated liquid cooling solutions are designed to reduce overall energy consumption.
- Liquid cooling also supports higher rack power densities.
- Organisations can scale for future AI and HPC deployments.
Which Liquid Cooling Solution Is Best for Small GPU Clusters?
When operating small AI environments, research labs, engineering teams, or departmental GPU clusters, organisations often prioritise ease of deployment and cost-effective scalability. Here are some key types of liquid cooling solutions which work best for high compute and GPU workloads.
Direct-to-Chip (DTC) Liquid Cooling
For enterprise AI deployments, direct-to-chip cooling is among the most widely adopted solutions. It is an advanced thermal management technique for data centres, in which specialised coolants circulate through cold plates mounted directly on high-heat components, such as CPUs and GPUs. Direct-to-chip cooling offers an effective balance of performance, cost, and deployment complexity for most small and medium-sized GPU clusters.
Here are some of the advantages:
- Compared to fully air-cooled environments, it drastically reduces the Power Usage Effectiveness (PUE).
- Cooling efficiency for processors is increased without replacing the entire infrastructure.
- It is compatible with many existing server platforms and is a practical retrofit option.
Rear Door Heat Exchangers (RDHx)
Without major infrastructure changes, organisations looking to modernise existing air-cooled facilities can consider rear door heat exchangers. This specialised rack-level cooling device replaces the standard rear door of a server cabinet with a liquid-cooled heat exchanger. This approach is particularly useful for businesses gradually increasing GPU capacity without redesigning the entire data centre.
Benefits include:
- Designed to handle high-density thermal loads by AI, blockchain and GPU workloads.
- Creates minimal disruption to existing server rooms.
- By replacing the existing door, they add no additional physical footprint to the data centre floor.
- Helps reduce the load on room-level computer room air conditioning units and improve the energy efficiency of the facility;
Which Cooling Technologies Are Better Suited for Hyperscale AI Factories?
Creating significantly greater cooling demands, hyperscale AI facilities operate hundreds or even thousands of GPUs continuously.
Immersion Cooling
A thermal management method where electronic components or entire servers are submerged directly into a specialised, electrically safe and non-conductive (dielectric) liquid. Thermal cooling is particularly well suited for extremely high-density AI training clusters, large-scale HPC environments, supercomputing facilities, and research organisations running continuous, compute-intensive workloads.
Benefits include:
- Dielectric fluid has exceptional cooling capacity, which dramatically saves energy.
- Server hardware can be packed much closer without space-consuming fans and heatsinks.
- Works with extremely low noise levels, making maintenance manageable.
- Protects hardware from airborne dust, humidity and corrosive gases.
Liquid-to-Liquid Cooling Systems
Using a centralised heat exchanger, liquid-to-liquid cooling systems transfer heat from equipment to a secondary liquid loop. Large enterprise and hyperscale facilities frequently deploy liquid-to-liquid cooling architectures. To transfer heat from IT equipment to the facility’s cooling infrastructure, these systems use secondary coolant loops.
Key benefits include:
- Superior efficiency, as water conducts heat better than air.
- Provide precise temperature control for process water and reject heat to outdoor dry coolers, while remaining cost-effective.
- Compared to traditional bulky fans and heat sinks, liquid cooling takes up less space, and operators can pack more computing power into a confined space.
- By preventing thermal degradation and performance throttling, it keeps hardware at stable temperatures.
What Factors Should Organisations Consider Before Selecting a Liquid Cooling Solution?
Choosing the right cooling architecture requires evaluating the entire operating environment, not just cooling performance. Here are some factors to consider:
- Workload intensity
- Rack power density
- Facility infrastructure
- Energy efficiency goals
- Scalability requirements
- Maintenance capabilities
Which Solution Delivers the Best Long-Term Value?
No single liquid cooling solution suits every data centre.
For small and mid-sized GPU deployments, direct-to-chip liquid cooling often provides the best combination of performance, cost-effectiveness, and ease of integration. While leveraging much of their existing infrastructure, they allow organisations to improve thermal performance.
On the other hand, for ultra-high-density computing environments and hyperscale AI factories, with properly sized CDUs, immersion cooling and large-scale liquid-to-liquid cooling systems are better suited to manage sustained thermal loads, maximising energy efficiency and operational scalability.
Conclusion
As AI adoption accelerates, selecting the right liquid cooling strategy has become a critical infrastructure decision. The ideal solution depends on workload intensity, rack density, facility capabilities, scalability objectives, and long-term operational costs.
Industry leaders such as Schneider Electric continue to advance liquid cooling technologies through integrated cooling, power distribution, and infrastructure management solutions that help organisations build scalable, energy-efficient data centres capable of supporting the next generation of AI and high-performance computing workloads.
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