As AI technologies continue to advance, data centres are being pushed to the edge, literally and figuratively, reshaping their operations to meet daily demands. According to LF Edge, the power footprint of edge devices is rising at a compound growth rate of 40%, from 1 GW in 2019 to 40 GW by 2028. With AI-driven automation, connected devices and intelligent systems expanding rapidly, enterprise edge AI will only accelerate this trend.
The role of prefabricated data centres
To meet the relentless demands of AI workloads at the edge, prefabricated data centre solutions offer a scalable, efficient and fast alternative to traditional builds. These modular, pre-engineered solutions enable rapid deployment, allowing operators to expand infrastructure without lengthy construction timelines.
Importantly, prefabricated solutions provide both cost and sustainability benefits making them an attractive choice for AI-era data centres.
Fast deployment
One of the biggest advantages of prefabricated solutions is speed. Modular units can be operational within months, compared to years for traditional data centres. Deployment time is typically 30% shorter, helping operators rapidly meet growing demands.
For example, Compass Data Centres partnered with Schneider Electric to utilise prefabricated infrastructure, significantly reducing deployment timelines and improving market responsiveness.
Scalability
Prefabricated data centres can be easily scaled to meet evolving needs. Hyperscalers may opt for prefabricated modular solutions when deploying data centres of 10 MW or greater, utilising designs that feature repeatable modules for electrical rooms and cooling spaces. This flexibility ensures infrastructure can grow alongside AI-driven workloads.
Efficiency and cost predictability
Standardised, pre-engineered components enhance efficiency and ensure consistent performance across deployments. Here, prefabricated solutions optimise cooling and energy use, achieving power usage effectiveness (PUE) levels below 1,5. Pre-assembled, factory-tested components minimise construction variability, reducing unexpected costs such as delays and material overruns. Predictable energy efficiency also allows operators to forecast long-term expenses, supporting better budget planning and ROI analysis.
Sustainability
Prefabricated data centres also offer significant sustainability benefits. Modular construction generates less waste than traditional builds, while optimised designs prevent energy and space inefficiencies. Remote monitoring and management enhance energy use, and prefabricated components can be disassembled and reused, promoting a circular economy.
Space optimisation
For organisations such as universities, hospitals and factories, data centre expansions can encroach on valuable operational space. Relocating to an external or less critical area allows businesses to maximise efficiency and optimise space utilisation. Prefabricated data centres can support AI in a number of ways:
• AI inference at the edge: Processing AI workloads at the edge reduces latency and enhances real-time decision making. It is also ideal for businesses adopting AI in applications such as chatbots, CRM systems, e-commerce and analytics.
• High-density prefabricated solutions: Designed for rapid deployment, high-density prefab solutions support complex AI workloads with minimal delay. These energy-efficient solutions reduce carbon footprints while delivering powerful computing capabilities where needed.
• Customised infrastructure for AI workloads: For large-scale AI applications, prefabricated solutions offer tailored infrastructure to meet specific power, cooling and computing requirements. These solutions support AI training models and intensive workloads in decentralised edge locations.
The future of AI, edge and modular data centres
As AI continues to evolve, modular data centres will play a crucial role in supporting edge computing advancements. Schneider Electric’s data centre experts have developed a modular data centre reference design to help organisations accelerate infrastructure deployment and meet the unique challenges of AI-driven workloads. To stay ahead in this landscape, operators need to integrate AI workloads with modular infrastructure strategies, driving efficiency, scalability and security.
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