From training billion-parameter models to handling real-time inference at scale, AI workloads are pushing data centers to the limit. Legacy infrastructure simply can’t keep up with the compute density, heat output, and performance demands of today’s AI applications. And with power and space at a premium, organizations can’t afford to build inefficiently.
The current pace of change makes future-proofing a moving target. As Jonathan Gough, Chief Data Scientist at Pellera, put it, “We’ve gone from AI 1.0 to AI 2.0 in two and a half years…and it’s not slowing down.”
The good news? You don’t have to overhaul everything at once. With the right design choices, you can incrementally future-proof your data center to meet AI’s growing demands without sacrificing scalability or ROI.
With that, let’s dive into five foundational tips that will help you build an AI-ready infrastructure designed to flex, scale, and deliver.
What Is an AI Data Center?
An AI data center is a specialized facility purpose-built to support the computing demands of artificial intelligence (AI) and machine learning (ML) workloads. Unlike traditional data centers designed for general IT or cloud services, AI data centers are engineered for high-throughput processing, low-latency data access, and power-dense hardware.
These environments are the backbone of modern AI innovation, enabling use cases like generative AI, natural language processing, computer vision, and large-scale model training. As enterprise adoption of AI accelerates, so does the need for infrastructure that can keep pace, both in terms of performance and sustainability.
What Does an AI Data Center Look Like?
AI data centers are distinguished by their high-performance architecture. They typically feature:
- Specialized hardware like GPUs, DPUs, and high-density CPUs for parallel processing
- Large-capacity NVMe storage systems to handle massive datasets
- Next-gen networking for rapid data movement across nodes
- Software orchestration tools for AI model training, tuning, and deployment
- Advanced cooling and power infrastructure to support unprecedented density
Together, these elements create an environment where AI workloads can run efficiently at scale, whether that’s training a multimodal foundation model or serving real-time inference for customer-facing applications.
How Much Power Does an AI Data Center Use?
AI data centers are energy-intensive by design. According to Goldman Sachs Research, the average AI data center consumes roughly 162 kilowatts (kW) per square foot, a figure that’s expected to grow to 176 kW per square foot by 2027.
These estimates account for the compute workloads alone and do not include power overhead for cooling, lighting, or ancillary infrastructure. When factoring in those additional systems, the total energy footprint becomes even more significant. This makes efficient design, thermal management, and sustainability a top priority.
5 AI Data Center Design Tips for Sustainable Performance
Designing for the AI era means preparing for complexity, not just capacity. From where your data lives to how your systems stay cool, every decision you make now affects your ability to support future models, scale responsibly, and manage costs.
These five tips offer a starting point for understanding the key data center design requirements for AI workloads, from heat and power to orchestration and talent.
Tip #1: Know Your Data Gravity
Before you spec out racks and servers, take a step back and ask: where does your data live, and where should compute happen? This concept, known as data gravity, helps organizations build smarter by aligning infrastructure to data location. The goal here is to set up your models for success from the start. As Jonathan noted, “The bigger the model, the further you can go, but it costs more, and you’re still going to run into a wall.”
Strong AI performance depends on a steady flow of high-quality, well-governed data. That means designing with the right backend in place:
- Scalable data pipelines that support continuous ingestion and transformation
- Tools for data governance and anonymization to manage sensitive information
- CI/CD processes tailored for AI workflows, not just traditional app delivery
Without this foundation, even the most powerful GPUs won’t deliver the results you’re after.
Tip #2: Build Compute Capacity Nearby
Not long ago, cloud-first was the gold standard. But for AI workloads, data egress fees and performance limitations have made the pendulum swing back. Enter reverse cloud migration: the trend of bringing workloads back on-premises to control costs, ensure privacy, and bring compute closer to high-volume datasets.
This shift is especially relevant for industries like healthcare, finance, or manufacturing, where real-time insights and data sovereignty are critical. Instead of designing for the cloud and adapting later, build AI data center compute capacity where your data already resides. You’ll reduce data movement, control costs, and improve response times, all without compromising performance.
As you move more compute to where your data lives, energy consumption becomes even harder to ignore.
Tip #3: Prepare for Advanced Power and Cooling Needs
The energy footprint of AI is nothing short of staggering. GPUs like the MI300X are pushing past 1,200 watts per unit, with rack densities soaring past 200kW. Traditional HVAC systems and air-cooled architectures simply aren’t built for this kind of workload.
To stay ahead, your data center must evolve across three fronts:
- Location: Sites in cooler climates or near renewable energy sources are becoming the go-to. Not just for cost efficiency, but for sustainability and long-term viability.
- Infrastructure: Liquid cooling is now table stakes. Raised floors and airflow won’t suffice when your thermal envelope triples. Specialized hardware accelerators like AMD Pensando DPUs can also help optimize east-west traffic and offload infrastructure tasks, a smart move as workloads scale.
- Power planning: Expect permitting challenges, local utility constraints, and capacity limits. Smart planning starts by modeling your future power draw, not just current usage.
>> Related Read – Carbon, Costs and Computation: The Compelling Case for Liquid Cooling
Tip #4: Realize AI Workloads Don’t Always Play Nice with Legacy Applications
AI workloads don’t run well on yesterday’s infrastructure. These models thrive on bare-metal Linux environments with containerized orchestration and parallel-processing power built in.
As Jonathan put it, “Prompting has its place, but it won’t get you to a production-grade application without serious infrastructure and orchestration under the hood.” That’s why many teams are rethinking legacy platforms like VMware in favor of architectures purpose-built for AI.
That mindset shift often starts with processors. AMD EPYC™ CPUs deliver the performance-per-watt today’s workloads demand, cutting power usage by up to 29% and reducing CAPEX by up to 46% compared to comparable Intel systems.
But the data center is only half the story. As tools like Microsoft Copilot move to the edge, AI PCs are becoming essential, bringing models on-device to reduce latency, preserve battery life, and personalize the user experience.
Whether you’re rebuilding or refreshing, future-ready environments are built to run AI at scale, wherever it lives.
Tip #5: Ensure Your Team Has Open-Source and Linux Skills
TensorFlow. PyTorch. Pandas. For many companies, these tools are the backbone of their AI software stack. And they live in the open-source world, on Linux-based environments that require a very different skill set than traditional enterprise stacks.
To keep pace, your IT team will need:
- Hands-on experience with bare-metal Linux and lightweight virtualization
- Fluency with package managers, public repos, and container registries
- A clear security strategy for open-source components and model supply chains
Recognizing these needs, many organizations are investing in their existing talent. In fact, according to the Linux Foundation, more than half of firms are doubling down on upskilling existing staff rather than relying solely on external hiring.
But even with internal training programs in place, scaling AI infrastructure can stretch resources thin. That’s why many organizations are also partnering with expert vendors to manage the full-stack AI data center, from orchestration to infrastructure and governance, especially when internal transformation feels overwhelming.
>> Related Read – Top 5 AI Adoption Challenges for 2025: Overcoming Barriers to Success
How to Build an AI Data Center with Confidence
Designing an AI-ready data center is one thing. Feeling confident about it is another.
Between evolving hardware demands, complex AI stacks, and the sheer speed of innovation, IT leaders are starting to look for more than just servers. They’re looking for a strategy, one that balances today’s pressures with tomorrow’s potential. As Jonathan emphasized, “You don’t have to go out and buy an AI and plug it in. But you absolutely, positively need to be taking account of where you’re at.”
That’s where Pellera comes in.
Whether you’re modernizing legacy infrastructure or launching a purpose-built AI facility, Pellera helps you move forward with clarity. Our experts work with your team to align technical design with business goals, streamline decision-making, and keep your roadmap ahead of what’s next.
When the stakes are this high, confidence comes from having the right partner.
Learn more about Pellera and how we can help bring your AI data center vision to life.
