What's the difference between AI Data Centres VS Traditional Data Centres?

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WHAT’S THE DIFFERENCE BETWEEN AI DATA CENTRES VS TRADITIONAL DATA CENTRES?

The infrastructure powering artificial intelligence is unlike anything built before. What once consisted of steady rows of servers supporting routine business operations has transformed into facilities designed not merely to store and process information, but to generate intelligence itself.

AI workloads, whether training massive machine learning models, running inference engines, or powering generative AI tools, demand energy and computational resources at a scale that dwarfs traditional IT requirements.

These workloads place unprecedented stress on data centre power distribution, cooling, and system design, requiring specialised hardware such as high-performance GPUs, AI accelerators, and ultra-fast interconnects.

AI data centre energy consumption is an engineering puzzle. Power distribution, thermal management, and airflow must all be redesigned to keep pace, requiring specialist expertise to ensure data centres remain reliable, efficient, and sustainable.

The question is no longer whether data centres must evolve, but how fast they can adapt to meet the demands of AI.

In this article, we’ll compare traditional and AI data centres, exploring key differences in power, cooling, design, and sustainability - and share how RED is helping clients manage this transition.

What is a data centre?

A data centre is a purpose-built facility that houses computing infrastructure to store, process, and distribute data across networks. They underpin almost every digital service, supporting everything from email and websites to business applications and cloud platforms.

Despite rapid changes in technology, the core functions of traditional data centres have remained consistent for decades. Storage systems archive and retrieve information, processing units manage computational tasks, and networking equipment connects both internal systems and external networks. Supporting this are power and cooling systems that ensure uptime, along with security and monitoring tools that safeguard data and keep services available.

The workloads in these environments are varied but steady. Web servers handle user requests, databases process transactions, and application servers run business software. File storage, email platforms, and resource planning tools make up most of the demand - relying on steady, reliable performance rather than short bursts of intensive computation.

To meet these requirements, infrastructure is optimised for efficiency and consistency. Servers with CPUs are designed for efficient step-by-step processing, rack layouts support standard air-cooling, and power use remains stable and predictable. This makes it easier to plan capacity and allocate resources effectively across different workloads.

While this architecture works for traditional workloads, it struggles under the demands of AI.

What is an AI Data Centre?

An AI data centre is purpose-built to meet the unique demands of artificial intelligence and machine learning, marking a major shift from the design of traditional data centres.

Where standard facilities are designed to support a wide range of general IT workloads, AI data centres are optimised for the heavy demands of training models, running inference engines, executing complex algorithms, and delivering real-time analytics.

The key difference lies in how these workloads are processed. Traditional applications typically run tasks sequentially, while AI relies on parallel processing to handle thousands of calculations simultaneously. 

To achieve this, AI data centres are built around GPU and TPU clusters, which are far better suited than CPUs to the matrix operations that power machine learning. However, this also means they demand significantly more power and generate far more heat, creating new challenges in power delivery and cooling.

Another defining feature is the network infrastructure. AI training requires ultra-fast, low-latency connections between compute nodes, alongside storage systems capable of moving vast datasets quickly and efficiently. If data can’t move quickly, AI training comes to a standstill.

Ultimately, AI data centres aren’t just a scaled-up version of traditional facilities - they are specifically engineered for sustained, high-intensity computing.

Key differences between AI data centres and traditional data centres

Both types of data centres share the same goal, but AI workloads create far greater demands - which fundamentally change how these facilities are designed and operated. Power, cooling, compute, storage, and networking all need to be optimised to handle continuous, high-intensity processing.

Infrastructure and design

Traditional data centres are built for general-purpose IT workloads, with racks typically consuming 5-15 kW. They can host a variety of applications using standard air-cooled racks and predictable layouts.

AI data centres, by contrast, demand much more power and density. GPU-heavy racks can draw 50-100 kW or more, demanding specialised solutions for power delivery, structural reinforcement, and heat management.

 Floors must support heavier equipment, cabling needs higher capacity, and rack layouts are carefully planned to accommodate both the weight and the heat of dense AI clusters. These facilities are designed from the ground up for high-density, sustained computing, leaving far less flexibility in rack configuration than traditional data centres.

Power consumption

AI workloads place extreme demands on a data centre’s energy systems. Training clusters operate under sustained, near-maximum load for extended periods, drawing almost as much power as an entire traditional facility. These workloads cannot be easily slowed down or balanced - they need constant, high-level energy to perform correctly.

AI data centre power consumption has significant future implications for what the data centre of the future will look like, especially as energy demand continues to climb. Industry forecasts suggest that by 2030, data centres could account for 3-4 %of global electricity consumption, with AI as a key driver.

Planning for this requires not only robust on-site power distribution but also consideration of grid capacity, renewable energy integration, and regional energy resilience. We’ve written about this at length in our article charting the roadmap to zero-carbon and water-negative data centres.

Cooling systems

Traditional data centres rely on air-based cooling, using CRAC units, hot/cold aisle layouts, and containment systems. This approach works well for CPU-driven workloads, where heat output is steady and predictable.

AI data centres, in contrast, produce intense, concentrated heat that air cooling alone cannot manage. Dense GPU clusters often require advanced solutions such as direct-to-chip liquid cooling, rear-door heat exchangers, or immersion cooling. Implementing these systems involves more complex distribution, careful planning, and additional space within the facility.

The environmental impact of cooling is a key factor in AI data centre design. Liquid cooling can increase water usage, which must be managed responsibly in regions with limited supply. At the same time, many data centre teams are adopting strategies to capture and reuse heat, improving energy efficiency and reducing environmental impact - strategies that we’ve covered in our own insights piece.

Project timelines and materials

Traditional data centre projects follow predictable supply schedules. Standard servers, cabling, and cooling systems are easy to source, making construction planning straightforward.

AI data centres are more complex. High-performance GPUs and accelerators can take months to arrive, while custom liquid cooling and high-capacity power systems need specialist suppliers. The additional weight and density of AI racks also call for reinforced flooring, advanced distribution equipment, and customised rack designs.

Because of this, AI projects usually take longer and involve more complex integration. Early planning, close collaboration with suppliers, and careful project management are essential to ensure all components arrive on time and fit smoothly into the construction schedule.

Sustainability

For traditional data centres, sustainability often focuses on Power Usage Effectiveness (PUE), integrating renewable energy, and optimising cooling. Steady, predictable workloads make these goals relatively straightforward to achieve.

AI data centres are a different story. Their constant high power demand, the embodied carbon of GPUs, and reliance on grid capacity all add to their environmental footprint. Advanced cooling systems can also increase water usage and have local environmental impacts.

Companies running AI data centres are taking a broader approach. On-site energy generation, energy storage, circular lifecycle management, and waste-heat recovery are becoming key parts of AI data centre design. The focus is shifting from just being efficient to running responsibly at scale.

Hybrid environments

Many organisations are adopting hybrid data centre strategies that bring AI and traditional workloads together in the same facility. This approach can improve operational efficiency but introduces new design and engineering challenges.

RED Engineering Design has significant experience in managing these complex environments. Our integrated design approach ensures that infrastructure supports both high-performance computing for AI applications and standard IT services, delivering reliable performance while maintaining operational efficiency and sustainability goals.

One of our recent projects, a 40MW data centre in Johor Bahru, Malaysia, shows this approach in action. The design included nine data halls, a prefabricated structure, and a 33kV substation to support both traditional IT and AI workloads within the same facility, giving the centre flexibility as computing needs change.

Advanced cooling, energy-efficient systems, and strong security kept the facility resilient across its hybrid environment.

This project highlights how RED balances the demands of high-performance AI with traditional IT, delivering scalable, reliable data centres built for the future of modern computing.

Conclusion

AI is transforming the data centre landscape, creating new demands for power, cooling, and infrastructure design that go beyond traditional IT requirements. While conventional facilities remain essential, AI workloads need high-density, energy-efficient, and resilient environments built for sustained performance.

For many organisations, the future lies in hybrid strategies that combine AI and traditional workloads, offering flexibility without compromising on performance or sustainability.

With extensive experience across complex, large-scale projects, RED Engineering helps clients navigate this transition. From optimising power and cooling to delivering scalable hybrid environments, we ensure facilities perform reliably today and can adapt to tomorrow’s demands.

Contact RED today to discover how our globally recognised expertise in data centre design can help you design and deliver future-ready data centres that balance performance, sustainability, and reliability.