AI is changing the rules of data center design, and liquid cooling is emerging as a critical piece of the puzzle
As artificial intelligence workloads grow more complex and power-hungry, traditional methods of keeping data centers cool are reaching their limits. That’s where liquid cooling comes in. Unlike conventional air-based systems, liquid cooling uses fluids to remove heat more efficiently from powerful computer chips. Here are five key things to know about liquid cooling technology — and why it’s critical for the future of AI infrastructure.
1. Why AI workloads need better cooling sytems
AI models — especially large language models (LLMs) and generative AI — run on specialized hardware like GPUs and accelerators, which consume much more power and generate more heat than traditional CPUs. When data centers host thousands of these high-performance chips, managing heat becomes a major challenge.
Air cooling alone often isn’t enough anymore. Fans and HVAC systems can struggle to maintain safe temperatures, leading to reduced performance, higher energy bills and even hardware failure. That’s why hyperscalers and AI-focused cloud providers are turning to liquid cooling technologies as a more efficient and scalable solution.
2. Liquid cooling is more efficient than air
Air is a poor conductor of heat compared to liquids. Water, for example, can absorb and transfer heat more efficiently than air. This allows liquid cooling systems to remove heat directly from the hottest components — like GPUs and memory chips — much faster.
As a result, liquid-cooled systems can run at higher performance levels without overheating, and can even operate in denser server configurations, saving valuable space in the data center. In many cases, liquid cooling can reduce total energy use by 10–30%, making it not just faster, but also greener.
3. There are different types of liquid cooling
There isn’t just one kind of liquid cooling. Today’s data centers mainly use three approaches:
Direct-to-chip: Coolant flows through plates mounted directly onto the chip, absorbing heat and moving it away.
Immersion cooling: Servers are submerged in a special non-conductive liquid that directly cools all components.
Rear-door heat exchangers: Chilled water flows through the rear doors of server racks, cooling the hot air as it exits.
Direct-to-chip is currently the most common in large AI data centers because it balances efficiency and cost. Immersion cooling, while extremely effective, requires specialized equipment and is still less widely adopted.
4. Liquid cooling lower carbon emissions and environmental impact
Cooling is one of the biggest contributors to data center energy use. It can account for up to 40% of a data center’s electricity bill. Liquid cooling helps reduce this burden by enabling more efficient heat removal, lowering reliance on large air conditioning systems.
Many companies are also starting to reuse the waste heat from liquid-cooled systems to warm office buildings or industrial processes, making operations more sustainable. In this way, liquid cooling supports broader ESG goals and helps reduce the carbon footprint of AI infrastructure.
5. The technology is rapidly scaling up
Liquid cooling is no longer just for experimental setups or niche workloads. Google, Microsoft, Meta, Amazon and Alibaba are all investing heavily in liquid-cooled data centers to support AI services. Intel and NVIDIA are designing chips optimized for liquid-cooled environments. Colocation providers and cloud firms are also offering liquid cooling-ready racks for clients running AI or HPC workloads.
According to market analysts, the global data center liquid cooling market is expected to experience a huge growth by 2030, driven largely by the rise of AI.
Conclusion
AI is changing the rules of data center design, and liquid cooling is emerging as a critical piece of the puzzle. It’s more efficient, environmentally friendly and ready for the extreme demands of modern computing. As AI continues to scale, so will the need for liquid cooling technology.
