New Conversations in Data Center Compute — Preparing Your Infrastructure for AI in 2026
For years, data center planning followed a familiar pattern. You refreshed servers every few years, added capacity when workloads grew, and focused on keeping everything stable and reliable. The conversation was predictable and manageable.
Todays conversations are changing because AI is introducing new workloads that need more from your infrastructure than traditional applications did Compute demands are rising, networking must be faster, and power and cooling are growing concerns.
IT leaders like yourself need to think how your environment will support an entirely new class of workloads. So, here’s a few questions to ask when preparing your infrastructure for AI.
How Should I Rethink Infrastructure for AI Workloads
AI workloads are different than traditional application. They process massive data sets and require infrastructure that provides GPU-accelerate compute, faster networking and storage platforms with fast data thruput.
Because of this, many organizations are asking if they should evaluate a dedicated AI infrastructure to bring components together into a single platform. Modern server platforms, such as HPE ProLiant systems, are increasingly being used for this purpose.
If you’re evaluating this kind of environment, one of the first questions to ask would be where the infrastructure should live.
Do your AI workloads work well in the cloud or will the work better on-premises where you have stronger security and controls of your sensitive data. Costs are another consideration, managing your own infrastructure can be better or worse depending on expertise, data movement and capital expense.
For many organizations, the answer ends up being a hybrid approach that allows you to take advantage of both.
What do I need to know about new Power and Cooling Options?
As you begin exploring AI infrastructure, you’ll quickly encounter another reality: these environments generate a lot of heat.
Modern GPUs and processors can push traditional infrastructure past their original design capabilities making the infrastructure inefficient and HOT!!! Because of this cooling is becoming a larger part of the conversation with innovators beginning to evaluate direct liquid cooling to support higher-density compute clusters.
Liquid cooling removes heat more efficiently than traditional air systems, leaving you with more powerful hardware with a lower energy footprint.
How does HPC Infrastructure Enable me to support AI Workloads?
Another noticeable trend is the growing use of HPC high-performance computing for AI applications. While HPC infrastructure was mainly designed for complex data sets, the infrastructure is now perfectly suited for AI needs.
Handling big data requires powerful compute, GPU support, and fast connections—all strengths of HPC platforms. Consequently, numerous organizations are opting to conduct AI model training, advanced analytics, and extensive data processing on high-performance computing (HPC) systems in conjunction with their conventional operations.
Planning AI infrastructure isn’t just about selecting the right servers. It requires careful consideration of compute architecture, networking design, power requirements, storage performance, and long-term operational strategy. At Comport, we partner with organizations to evaluate their current environments and design infrastructure platforms that are prepared for the next generation of workloads. Whether you’re just starting to explore AI or are ready to scale new initiatives, our team delivers guidance and solutions to help you build an environment that ensures optimal performance, efficiency, and long-term growth.
