How AI factories are changing the way we build data centres

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HOW AI FACTORIES ARE CHANGING THE WAY WE BUILD DATA CENTRES

Key insights:

• AI factories are GPU-optimised, high-density facilities built for large-scale intelligence production.
• Traditional data centre designs can’t meet AI’s power, cooling, or performance demands.
• Sustainable AI infrastructure must address embodied carbon, water use, and grid resilience.
• Early adopters gain an edge; legacy systems risk obsolescence in the AI economy.

In server rooms across the globe, a quiet transformation is underway. Where there were once rows of general-purpose servers humming through routine workloads, new systems designed for a totally different challenge are now taking their place - AI factories.

While traditional data centres were built to host websites, store files, and support businesses, AI factories serve a fundamentally different purpose: they manufacture intelligence at scale.

These high-density, GPU-optimised hubs process massive amounts of raw data in order to train and operate complex AI models that can learn, predict, and make decisions in real time. As the backbone of artificial intelligence infrastructure, these facilities are engineered to deliver the immense computing power that today’s systems need.

With global AI demand reshaping the infrastructure landscape, the question isn't whether to adapt - it’s how quickly we can design, engineer, and deliver the environments capable of supporting this next generation of digital services.

This transformation requires a fundamental rethinking of power, cooling, and MEP systems - exactly the expertise RED brings to this rapidly evolving sector.

What is an AI factory and how does it work?

An AI factory functions like a manufacturing line: data flows in as raw material, GPUs act as engines, and the output is real-time, predictive intelligence.

The architecture is built around large-scale GPU clusters that demand a different way of thinking about infrastructure. Instead of individual servers, entire racks operate as unified compute units, requiring high-density integration, specialist cooling strategies, and extremely low-latency connectivity across the facility.

High-speed interconnects enable low-latency connectivity across thousands of components, preventing bottlenecks that could cripple parallel processing operations.

AI factories are designed to support the full lifecycle of artificial intelligence, ingesting data, training models, and deploying inference engines, all within a single, resilient, and highly optimised facility.

What is the difference between AI data centres and normal data centres?

The difference between AI data centres and traditional ones comes down to how they're designed.

Traditional data centres rely on CPUs to process tasks sequentially - perfect for websites, databases, and everyday business software. AI factories use GPU and TPU technology instead, which can handle the intense, simultaneous processing that AI and machine learning demands.

This creates entirely different infrastructure needs. AI systems require much more power and generate far more heat than traditional data centres can handle.

Regular data centres run at steady, predictable power levels. AI factories, however, need to support much higher power demands, which creates intense heat that standard cooling systems simply can't manage. This means AI facilities need completely new approaches to power distribution and cooling.

These demands are changing how data centres are designed. A single rack in an AI factory can use 100kW of power - ten times more than traditional data centres. The high-speed networking required for AI is also expensive, and in large-scale AI projects, this part alone can take up a major share of the total spend.

AI fundamentally changes the data centre model. Instead of handling many different types of tasks, everything in an AI factory is designed for one goal: keeping AI systems running at full capacity, 24/7.

What infrastructure is needed for AI factories and data centres?

The infrastructure requirements for AI introduce a level of complexity that goes far beyond traditional data centre technology. As AI adoption accelerates, global data centre power consumption is projected to more than double by 2030, with AI alone expected to drive usage from current levels of 1-2% to as much as 3-4% of total electricity demand.

Supporting these workloads requires power systems capable of delivering multi-megawatt loads consistently and without compromise. High-voltage distribution, resilient grid integration, and backup power systems are essential to maintain continuous operation.

Cooling requirements are just as critical. Traditional air-cooling approaches simply cannot remove the heat generated by dense GPU clusters operating at full capacity. Advanced cooling strategies, from direct liquid cooling to immersion systems, become necessary rather than optional. These systems must be engineered for reliability, efficiency, and scalability as AI workloads continue to intensify.

These pressures make MEP coordination increasingly complex. With space at a premium and build programmes moving at speed, the integration of power, cooling, and high-speed networking systems must be planned and executed with absolute precision.

At RED, we deliver AI-ready infrastructure that meets these demands head-on - combining deep technical knowledge with a whole-systems approach to design facilities that are resilient, efficient, and built to support the next generation of digital workloads.

The importance of building lasting artificial intelligence infrastructure

Designing sustainable AI facilities requires more than energy optimisation alone. Metrics like Power Usage Effectiveness (PUE) are important, but they only tell part of the story. 

The embodied carbon of GPUs and TPUs must be included in lifecycle assessments, given the significant emissions associated with their manufacture. Water use in advanced cooling systems also demands localised strategies for reduction, reuse, and long-term environmental management.

Meeting sustainability goals is particularly challenging in high-performance AI environments. High, continuous power demand can strain local grids - particularly when relying on renewables, which don’t always keep up with demand. As a result, on-site generation and battery storage play a critical role in maintaining reliability while reducing dependence on grid-supplied power. 

At RED, we engineer AI infrastructure with sustainability in mind from the outset. Through our comprehensive sustainability audits, we provide the insights needed to create future-ready facilities. 

For a closer look at how these principles can shape future-ready facilities, read our article: The Roadmap to Zero Carbon, Water Negative Data Centres.

The risk of not adapting to the change in data centre technology

Failing to adapt to the changing infrastructure requirements of AI factories presents a growing risk for many organisations. 

Traditional data centres are not equipped to support AI workloads at today’s scale and intensity, leading to higher operational costs, reduced efficiency, and missed opportunities to capitalise on AI for strategic advantage. Businesses relying on traditional infrastructure may be seen as falling behind, particularly as buyers increasingly prioritise AI-ready solutions. 

Rapid advances in AI and costly hardware make the risk of outdated technology very real. Without continuous upgrades to infrastructure, expensive systems risk becoming outdated quickly, resulting in poorer performance and substantial financial impact.

Early investment in AI-optimised infrastructure, however, gives businesses access to faster innovation. Early adopters benefit from streamlined operations, enhanced service delivery, and a strong competitive edge in the emerging intelligence economy.

Partner with Red Engineering Design to build AI infrastructure that lasts

Artificial intelligence is rapidly redefining the physical infrastructure that powers our digital economy. RED is already building this future, delivering facilities that balance performance, resilience, and sustainability for the most demanding AI workloads.

Our MEP systems expertise is specifically focused on meeting the high-density, mission-critical requirements of next-generation AI factories. We prioritise delivering scalable and sustainable solutions, understanding that reliable infrastructure is key to future-proofing.

Get in touch with RED to discuss how we can help you deliver infrastructure that meets the power, cooling, and density demands of modern AI factories.