Accelerate AI factory design with proven infrastructure blueprints

In a data hall or cluster-scale optimization rather than a near-term requirement for the entire site. If your existing infrastructure is AC-based, the near-term question is not whether to rip and replace, it is where to introduce DC conversion and how close to the rack you can place it. The practical tradeoff is between preserving upstream AC distribution, adding rack-adjacent power sidecars, and reserving enough physical and electrical space to support future 800 VDC loads. Sidecar or power-rack approaches allow operators to host 800 VDC equipment without converting the whole data center to DC, which matters because early 800 VDC adoption will apply to specific high-density AI clusters, not every load in the building. Larger future expansions may justify more centralized 800 VDC support, but that becomes

The key tradeoff here is flexibility versus isolation. A shared power chain can give operators more room on days when mechanical load drops (e.g., when chillers are not working as hard and more of the available capacity can support IT load). A separated architecture gives the IT side a cleaner boundary for future changes, including 800 VDC upgrades, but it also makes mechanical power harder to reallocate because that capacity sits inside its own train. Before committing to either structure, operators should pressure-test seasonal operating modes, maintenance scenarios, generator loading, CDU power paths, and the expected split between air-cooled, liquid-cooled, and future DC-powered IT.

Not necessarily. UPS and BESS serve different purposes, and whether BESS can replace a UPS depends on the AI workload’s availability and ride-through requirements. For AI applications that cannot tolerate interruption, such as high-availability inference workloads, a UPS remains the primary layer of protection because it provides instantaneous ride-through, power quality support, and protection against short-duration disturbances. BESS serves a complementary role by enabling energy optimization, peak demand management, grid services, renewable energy integration, and longer-duration stored energy. In many AI facilities, BESS strengthens the overall energy strategy, while the UPS continues to protect critical loads from interruption. Before replacing a UPS with BESS, organizations should carefully evaluate workload tolerance, resiliency requirements, and ride-through expectations.

For guidance on BESS use cases, architecture considerations, and adoption criteria, read our white paper, Understanding BESS: Battery Energy Storage Systems for Data Centers.

Schneider Electric's guidance for data centers similarly positions BESS as a tool for resiliency, sustainability, and energy optimization rather than a direct replacement for critical power protection

Prefab can cover more than power skids, but operators should think in terms of which site activities they want to remove from the critical path. Major prefabricated elements can include medium-voltage gear, e-houses, low-voltage power trains, Power Skids, IT pods, integrated racks, packaged chillers with onboard pumps, and selected piping or service-hallway assemblies. The practical value comes from reducing field coordination among electrical contractors, mechanical contractors, construction managers, and commissioning teams. Power Skids can integrate UPS, switchgear, batteries, ATS, and management software, with listed configurations including 1,250 kW at 480 V/60 Hz and 2 MW, 2.25 MW, and 2.5 MW 400 V models.

A useful way to think about reference designs is not “standard versus custom,” but “validated starting point versus blank-sheet engineering.” AI-ready reference designs can shorten the planning cycle by giving project teams a pre-engineered baseline for power, cooling, IT layout, and controls integration . Current Schneider Electric reference designs include 7.392 MW NVIDIA GB200 NVL72 liquid-cooled AI clusters, 7.536 MW NVIDIA GB300 NVL72 designs, 10 MW–12.4 MW Vera Rubin NVL72 designs, 1 MW 12-rack prefab AI solutions and AMD Helios Rackscale Solution powered by AMD Instinct™ MI455X GPUs. The tradeoff is that modular elements can be prefabricated in major chunks, while the complete site architecture still requires integration across utility interconnect, mechanical plant, medium-voltage distribution, IT deployment strategy, and construction sequencing.

You can use redundant capacity for more compute, but operators should treat that as an operating-mode decision rather than free capacity. Traditional concurrently maintainable facilities reserve electrical and mechanical headroom so systems can come offline for maintenance while the committed IT load stays online. AI changes the economic pressure because grid capacity, deployment speed, and compute demand make idle redundant capacity harder to justify. For example, a concurrently maintainable facility may be capable of operating above its committed IT load, but doing so consumes capacity that was originally reserved to maintain resilience during maintenance activities or failure events. The real decision is whether the business values additional tokens per facility more than it values the original redundancy strategy. Before tightening margins, define the operating state explicitly: normal redundant mode, high-throughput mode, maintenance mode, and failure response mode.

The workload should drive the design once the operator knows whether the site will support general colocation, high-density neocloud customers, training clusters, inference platforms, or a mix of those models. A general colocation facility may need to preserve optionality at the bulk power and cooling level until tenant requirements become clear. A focused AI cloud or single-purpose AI factory can design directly around a known GPU roadmap, cluster topology, rack density, and cooling requirement. Schneider Electric’s AI data center offer frames the same problem around integrated infrastructure for cooling, power, software, pod and rack systems, and lifecycle services for high-performance AI workloads.

Do not design the entire data center as if every load will become 800 VDC. The practical design question is where 800 VDC enters the architecture and how large the DC conversion block needs to be. Early deployments can use rack-adjacent sidecars, often aligned to one or two GPU racks, inside an existing AC data center without a major upstream retrofit. Larger future clusters may justify centralized 800 VDC blocks in the 2 MW to 5 MW range, potentially scaling toward larger chunks, while longer-term architectures may bring medium voltage into solid-state transformer-based conversion at roughly 10 MW-class scale. 800 VDC architectures address rack densities moving beyond 400 kW by relocating AC-to-DC conversion outside the IT rack and using power racks or sidecars to reduce disruption in existing AC-based facilities.

The key tradeoff is isolation versus dynamic capacity sharing. Separate power trains can simplify boundaries between IT and cooling infrastructure, especially when large chillers, CDUs, and data halls do not align cleanly in the same increment. Shared infrastructure can preserve flexibility during free-cooling conditions because unused mechanical capacity may be redirected toward IT load if the transformer and downstream architecture allow it. The risk with full separation is stranded power: if mechanical load drops but the IT transformer already operates at its limit, the operator cannot recover that unused cooling-side capacity for compute. Gap to flag: this design choice needs site-specific modeling around data hall size, chiller block size, transformer allocation, redundancy target, and seasonal operating modes before it becomes a defensible standard.

Electrical, airflow, and liquid flow modeling should happen before finalizing the design because each model addresses a different class of failure. Electrical modeling validates fault current, protection, safety, reliability, and what-if scenarios around maintenance or added load. CFD or airflow simulation validates containment, air-side capacity, and redundant cooling behavior. Piping and fluid-flow modeling validates whether every rack receives the required coolant flow during normal, redundant, and maintenance states. The next step is coupling IT load behavior to facility behavior, so AI load fluctuations can propagate through power systems, CDUs, chiller controls, and cooling loops before the site operates under real load.