Modernizing your data center with AI servers: 7 questions for legacy infrastructure

You’ve already made a strong start by successfully running an AI pilot in the cloud — whether driven by innovation or budget-savvy thinking. Now, you're aiming higher: deploying a private AI model to transform key business or technical processes and gain a competitive edge. 

To move forward, you’ll need to carefully balance priorities like accuracy, privacy, speed, and scalability. Choosing between a fully private on-premises setup or a hybrid cloud model is crucial. While managing AI workloads can be complex, especially for first-time deployments, understanding the main challenges will help you plan effectively. 

Retrofitting or deploying AI servers in your legacy data center? Here are the 7 key questions you should ask yourself:

1. Will my existing IT racks be compatible with new AI servers? 

2. Will my existing rack Power Distribution Unit (PDU) support new AI servers? 

3. Can I use my existing power distribution infrastructure? 

4. Do I have enough utility power to support an AI compute stack? 

5. Are the latest AI servers available without liquid cooling? 

6. What constraints can I expect from air-cooled AI servers? 

7. What components are required to build a liquid cooling architecture for AI servers? 

Deploying AI at scale requires more than just new servers — it demands a thoughtful redesign of your infrastructure. As compute density rises with each GPU generation, upgrades to racks, power systems, and cooling — especially liquid cooling — become essential for performance and reliability. 

Whether you're aiming for high-performance GPUs or a lower-density setup, assessing your current infrastructure is key. By planning for cooling, power, and compatibility, you’ll be better positioned to transition from pilot to production and unlock the full ROI of private AI deployment.