Artificial Intelligence workloads introduce a fresh order of magnitude in terms of cooling. Compared to typical enterprise IT, AI clusters contain more computing power within fewer racks and generate significantly more heat. With training and high-end inference workloads, it is not unusual to witness heat densities increase from 6–12 kW per rack to 30 kW or higher.
These systems build a dynamic temperature profile that demands to be cooled with higher-density cooling strategies. The issue becomes whether air-based cooling, traditional or not, will be able to carry the load, or if liquid cooling techniques will have to be used. This will be based on many factors such as rack power, workload cycles, the design of the room, and long-term growth. It’s evident that conventional air cooling may not be sufficient as AI workload keeps on changing and growing.
Comprehending Air Cooling Solutions
Even though the concept of air cooling is not new, modern techniques are far more sophisticated than simply placing traditional CRAC (Computer Room Air Conditioner) units in a room. In order to meet the demands of contemporary IT environments, modern systems have expanded. The way that cutting-edge air cooling solutions provide effective thermal management is explained below:
Targeted Heat Management: Upcoming methods focus on sending cool air straight to the source of heat. To successfully separate hot and cold air streams, it comprises establishing short air paths, positioning cooling modules in rows, and utilizing containment strategies.
Improved Accuracy: Precision Air Conditioners, like Liebert CRV, reflect this improvement. They utilize “in-row” cooling, with units housed between server racks. This allows for highly focused airflow, accurate temperature control, and efficient humidity management—abilities critical in the high-density environments of today.
Flexible Cooling Solutions: The systems have a lot of flexibility with several cooling medium alternatives. Solutions can use chilled water, alternate refrigerants, or glycol-based systems based on the unique infrastructure of a facility.
Seamless Integration and Efficiency: If implemented correctly, contemporary air cooling systems readily control temperatures and solve thermal issues. In many cases, this can be done without necessitating the total remodeling of a room, offering an efficient and less invasive upgrade solution.
When to Use Liquid Cooling
After the generated heat from the hardware exceeds what air cooling can handle, liquid cooling is a solid option. One of the most effective techniques is direct-to-chip liquid cooling, wherein coolant is delivered directly to the processors or GPUs and heat is dissipated at the source. Another is the rear-door heat exchanger, which installs at the rear of the server rack and pre-empts heat before it can spread back into the room.
For very high-density applications, like AI training pods, immersion cooling might be an option, where servers are immersed in a dielectric fluid. Liquid cooling choices are based on water temperature, facility water availability, and serviceability requirements. A proper liquid cooling strategy can considerably enhance the performance and efficiency of cooling systems, particularly where normal air cooling is not enough.
How to Select the Right Cooling Solution
Choosing the right cooling solution for your needs can depend on several factors. Here’s a simple guide to help match your needs with the appropriate cooling method:
| Cooling Approach | Ideal Rack Density | Suitable Use Cases |
| Advanced Air with Containment | ~10–25 kW | Light AI workloads, mixed-use, small labs, retrofits. |
| Rear-Door Liquid Assist | ~20–60 kW | High-density inference, retrofits for AI clusters. |
| Direct-to-Chip / Immersion | ~40–100 kW+ | Intensive AI training pods, new AI deployments. |
These estimates help you decide between traditional air cooling and liquid cooling solutions. As AI workloads increase, hybrid solutions combining both air and liquid cooling will become more common, providing flexibility to scale as needed.
Real-Life Examples of Cooling Solutions in Action
Let’s look at some examples where different solutions are being used effectively:
Edge inference room in healthcare (15–20 kW/rack): In small spaces like healthcare settings, row-based air cooling systems with full containment are ideal. This setup helps control the temperature while ensuring the comfort of staff and patients. A controlled environment is maintained even as workloads fluctuate.
AI lab retrofit (25–40 kW/rack): For existing facilities, adding a rear-door heat exchanger can be an effective way to improve cooling without having to redesign the entire infrastructure. By adding rear-door systems to the densest racks, heat is captured and removed more effectively, which prevents equipment from overheating.
Greenfield AI pod (40–100 kW+/rack): For large AI training environments, direct-to-chip liquid cooling is often the best option. These systems are designed to manage extremely high densities, keeping the environment cool and efficient. When paired with air cooling for residual heat, these solutions can ensure consistent performance.
As AI densities continue to grow, these hybrid and liquid solutions will become increasingly common.
Checklist for Cooling Solutions
Here’s a handy checklist for when you’re considering your cooling options:
Keep air paths short: Position cooling equipment close to the IT racks to minimize airflow loss. Proper containment helps improve efficiency by ensuring air isn’t bypassed.
Use sensors for accuracy: Install temperature and pressure sensors in the racks to monitor conditions closely. This data allows for better control and tuning of your cooling system.
Plan for liquid cooling in the future: Even if you start with air cooling, make provisions for adding liquid cooling in the future. Installing necessary piping and valves early can make future upgrades easier and less disruptive.
Optimise controls for AI workloads: cooling systems ought to have the ability to adjust their output in response to real-world circumstances. Air systems should adjust airflow independently, while liquid systems should be able to handle varying loads efficiently.
Think about serviceability: Your racks may gain additional depth and weight from liquid cooling systems, particularly rear-door units. Make advance plans to ensure that these systems are properly accessed and supported.
Plan for growth: It’s essential to prepare for a future increase in densities even though your data center may not yet have 30 to 40 kW per rack. Plan for growth while you design your infrastructure.
Final Thoughts
The high demands of Artificial Intelligence workloads revolutionize the way we manage thermal in IT systems. With heat densities still increasing, the choice of the appropriate precision cooling solution – advanced air-based solutions or advanced liquid-assisted solutions – becomes even more critical. At the end of the day, guaranteeing efficient heat extraction is critical to achieving optimum performance, extending equipment lifespan, and ensuring the sustained reliability of your mission-critical AI infrastructure.
When it comes to selecting and implementing these essential cooling systems for your AI environment, Meghjit Power Solutions offers a comprehensive range of choices. Our proficiency in deploying both precision air conditioning, including advanced in-row cooling systems, and cutting-edge liquid-assisted cooling solutions (like direct-to-chip and rear-door heat exchangers) ensures your data center or AI pod operates at peak efficiency even as workloads grow. With the Vertiv “Emerging 1-Phase Contribution Partner” award under our belt, Meghjit Power Solutions is recognized for excellence in delivering energy-efficient solutions for high-performance computing. Visit us at our website today to explore solutions we offer and secure the support needed to meet tomorrow’s AI demands.
