Prefabricated modular datacentres: Unlocking the AI infrastructure bottlenecks
In this guest post, Nick Ewing, managing director of datacentre design consultancy, EfficiencyIT, sets out why modular designs could be key to unlocking the development bottlenecks slowing the pace of new AI infrastructure deployments coming online
Artificial intelligence (AI) investment across the UK and Europe is accelerating faster than most organisations anticipated. Enterprises are committing to AI strategies, governments are prioritising digital infrastructure, and hyperscalers are expanding capacity at a rate that would have seemed extraordinary just a few years ago.
Yet beneath this ambition lies a problem that investment alone cannot easily fix: the large-scale infrastructure needed to support AI is not being built fast enough.
This isn’t a technology problem. The hardware exists. The software exists. What’s missing is the physical infrastructure to deploy it quickly, reliably, and where organisations need it.
Traditional datacentre construction hasn’t kept pace. A conventional build, from planning approval to operational readiness, typically takes 18 to 36 months. In technology terms, that’s a lifetime.
By the time a facility is complete, the workloads it was designed to serve may have evolved significantly.
Meanwhile, grid connection timelines in major UK and European markets have extended to five years or more in some locations, creating a bottleneck that construction expertise cannot overcome.
For organisations trying to deploy AI compute at scale, these delays are more than inconveniences. They are existential threats to competitiveness.
The changing nature of AI infrastructure demand
AI workloads are fundamentally different from conventional IT. A standard datacentre rack historically drew five to ten kilowatts of power. Today’s GPU-intensive AI racks commonly require 40 to 80kW, with some high-density configurations approaching 100 to 150kW or more. Advanced AI models require four to eight times more power per rack than traditional IT equipment, and training runs can sustain these loads for weeks or months.
Legacy infrastructure wasn’t designed for this. Retrofitting existing facilities to accommodate these densities is expensive, disruptive, and often not viable within the timeframes AI deployment demands. Organisations that try to fit AI workloads into conventional datacentres frequently encounter thermal limitations, power distribution bottlenecks, and cooling systems never intended to cope with this level of intensity.
The result is a widening gap between AI ambition and infrastructure reality, one that conventional construction models cannot close at the required pace.
What modular infrastructure delivers
Prefabricated modular datacentres have changed considerably from their origins as containerised stopgaps. Today’s solutions are sophisticated, enterprise-grade infrastructure systems, precision-engineered in controlled factory environments, tested before delivery, and purpose-built for the demands of AI workloads.
The most significant advantage is speed. Where a traditional build requires 18 to 36 months, a prefabricated modular facility can be operational in 12 to 16 weeks. And that’s a fundamental change in what’s possible for organisations that need to deploy capacity now rather than in three years.
Factory construction also delivers quality and predictability that on-site builds struggle to match. Modules are assembled in controlled conditions, eliminating many variables that drive cost overruns and quality inconsistencies in traditional construction.
Power, cooling, fire suppression, and monitoring systems are integrated from the outset rather than retrofitted later. This integrated approach also reduces construction-related waste, with some estimates suggesting scope 3 emissions savings of 30% or more compared to conventional methods.
Scalability is another critical differentiator. Rather than committing to a fixed capacity at the outset and either overprovisioning or constraining growth, modular infrastructure allows organisations to deploy capacity in line with actual demand. Additional modules can be brought online as workloads grow, keeping capital expenditure aligned with operational reality.
Deploying in complex and sensitive environments
For organisations operating in regulated, security-sensitive, or remote environments, modular infrastructure offers capabilities that conventional builds cannot match.
Modules can be deployed in locations with limited or unreliable grid access, incorporating battery storage, generator systems, and renewable generation where appropriate. They can be engineered to operate across a wide range of environmental conditions while maintaining optimal internal environments for sensitive AI hardware.
This flexibility matters for organisations that need to deploy compute at the edge, at distributed locations across a network, or in environments where planning restrictions or grid constraints rule out conventional construction.
Infrastructure as a strategic question
The organisations that will lead in AI are not necessarily those with the largest budgets or the most advanced models. They are the ones that can deploy compute reliably, at the pace technology demands, and in the environments where it’s needed. Infrastructure has become a competitive variable in a way it wasn’t five years ago.
Prefabricated modular datacentres don’t solve every challenge in the AI infrastructure landscape. But for organisations facing the pressures of deployment speed and grid constraint, they offer something conventional construction cannot: the ability to move from decision to operational capability in weeks rather than years, without sacrificing the reliability, power density, or scalability that AI workloads require.
In the AI era, infrastructure agility is no longer a nice-to-have. It’s the foundation on which competitive advantage is built.