Nexalytics Tech
Building a traditional hyperscale data center from scratch — securing land, permits, a dedicated utility substation, and multi-year construction — typically takes two to three years. That timeline has become a real bottleneck as demand for AI compute keeps outpacing new supply, and it's a big part of why the industry has shifted hard toward prefabricated, modular data centers: units built and tested in a factory, shipped in sections, and assembled on-site in weeks rather than years.
What "modular" actually means in practice
Modular data centers move the complexity of construction off the job site and into a factory. Power distribution, cooling plant, and IT racks are pre-integrated into standardized, containerized or building-block modules before they ever reach the deployment location. Industry group reporting on well-executed modular projects points to schedule improvements of 30–50%, compressing typical delivery windows from 24–36 months down to roughly 12–16 months; some vendors cite even faster results for smaller deployments. Delta Electronics, a major power and cooling supplier, showcased a prefabricated AI modular data center at COMPUTEX 2026 that it says cuts deployment time by up to 60%, combining 800-volt DC in-row power with 3-megawatt liquid cooling systems built for high-density GPU racks. Separately, industry trackers have documented a prefabricated AI computing center completed in 120 days that achieved a power usage effectiveness (PUE) rating of 1.25 — a meaningful efficiency figure, since traditional air-cooled facilities typically run in the 1.4–1.6 PUE range, meaning less of the electricity going into the building is wasted as overhead rather than powering compute.
Liquid cooling is the real enabler
The push toward modular construction is inseparable from a parallel shift to direct liquid cooling. AI training and inference racks now routinely draw far more power per rack than a conventional enterprise server room was ever designed for, and air cooling struggles to keep up at that density. Vendors including Vertiv (with its MegaMod HDX modules) and Schneider Electric (EcoStruxure Modular Data Centers) now build direct-to-chip liquid cooling, rear-door heat exchangers, and integrated power distribution directly into their prefabricated products, rather than treating cooling as something added after the fact. Industry engineering guidance suggests integrated liquid-cooled facilities can push PUE down toward roughly 1.10 in the best cases, compared with 1.4–1.6 for conventional designs — cutting both the electricity bill and the water use tied to evaporative cooling towers.
Who is actually building these
The vendor landscape has consolidated around a handful of established power and infrastructure names rather than a wave of pure-play startups. Schneider Electric's EcoStruxure Modular Data Center line offers all-in-one modules, prefabricated IT rooms, and dedicated power and cooling modules that can be mixed and matched for a given deployment. Vertiv's MegaMod HDX bundles direct-to-chip liquid cooling with power distribution in a single ready-to-deploy module aimed specifically at AI workloads. Delta's COMPUTEX 2026 showcase paired its prefabricated modular design with 800-volt DC in-row power and what it calls "GoCool" 3-megawatt liquid cooling units, explicitly targeting the thermal challenges of high-density GPU racks and next-generation chip-on-package servers. Huawei has also documented a 120-day prefabricated AI computing center build in industry market research, achieving the 1.25 PUE figure and over 3.4 million kilowatt-hours in annual electricity savings compared with a conventional design of similar scale.
Training versus inference — different problems, same answer
The infrastructure needs of AI training and AI inference are diverging. Training still concentrates in centralized, hyperscale campuses built for sustained, extreme power draw. Inference, especially for products that need to respond to users in real time, increasingly needs to sit closer to where people actually are — which favors smaller, regional, and edge-sited facilities rather than one enormous campus. Modular, prefabricated units are a natural fit for that shift: they can be sized appropriately for a regional inference cluster and deployed faster in locations where a full hyperscale build would be impractical or where local communities and utilities are resistant to a mega-facility's power and water demands.
Market researchers estimate the modular data center market at roughly $38.8 billion in 2026, projected to grow to around $102 billion by 2032 at a compound annual growth rate near 17%, reflecting how central this approach has become to the industry's near-term expansion plans, not just a niche workaround.
- Traditional hyperscale build: 24–36 months; modular builds: often 12–16 months or faster
- Delta's prefabricated AI modular data center: up to 60% faster deployment (COMPUTEX 2026)
- Documented 120-day prefab build achieved a 1.25 PUE rating
- Integrated liquid cooling can push PUE toward roughly 1.10 versus 1.4–1.6 for air-cooled designs
- Modular data center market: ~$38.8B in 2026, projected ~$102B by 2032
Our take
Modular construction isn't a stopgap while the industry waits to build "real" mega-campuses — it's becoming the default approach because permitting, grid capacity, and community pushback have made the old multi-year hyperscale playbook too slow for how fast AI demand is moving. The efficiency gains are real and independently documented, though PUE figures and deployment-time claims still come mostly from vendors marketing their own products, so independent, third-party verification of the biggest claims (the 60% figure especially) would strengthen the case further.
What to watch next
Watch for how quickly modular deployments scale beyond pilot projects into the bulk of new capacity additions, whether utilities can keep pace with a larger number of smaller, distributed power connections instead of a few massive ones, and whether standardization emerges across vendors — right now, a power module built to one hyperscaler's specification often can't integrate with another's infrastructure without a custom coordination study.
