AI factory demands more. We deliver.
Schneider Electric: the only end-to-end AI factory partner - from grid-to-chip, chip-to-chiller, for scalable AI.
Schneider Electric, together with a global ecosystem of partners, delivers proven solutions, software, and services to design, power, cool, and operate AI factories at scale.
Working in collaboration with all the leading GPU and TPU manufacturers, including NVIDIA, we are leading the innovations that are reshaping how data centers are built, operated and maintained.
Schneider Electric’s AI factory solutions include fully engineered designs, digital twin, liquid and air cooling, high-density power, modular pods, racks, software, and lifecycle services all designed for AI-ready data centers.
Based on our cooperation and interoperability with NVIDIA and OpenUSD, our AI factory solutions scale efficiently and work seamlessly across the entire AI infrastructure.
Validated AI data center reference patterns (ANSI/IEC) + selection guide.
OpenUSD digital twins with ETAP and NVIDIA Omniverse for AI infrastructure modeling.
800 VDC architectures, rack-level power, and resilient energy distribution.
Direct-to-chip, immersion, and hybrid cooling for high-density AI workloads.
Modular pods and white-space modules (time-to-ready).
DCIM, planning/modeling, alarm management, OT cybersecurity.
Teaming up with NVIDIA for AI Factories
Schneider Electric teams with NVIDIA to develop validated blueprints to design, simulate, build, operate, and maintain gigawatt-scale AI factories.
Launching new data center solutions
Schneider Electric launches new data center solutions to meet challenges of high-density AI and accelerated compute applications.
Digital twin for AI factory power requirements simulation
ETAP and Schneider Electric unveil the world’s first digital twin to simulate AI factory power requirements from grid to chip level using NVIDIA Omniverse.
AI digital twins with OpenUSD for lifecycle operations
Scaling AI enabled digital twins across the data center lifecycle with OpenUSD and enabling seamless lifecycle interoperability.
Accelerating AI factories scaling with NVIDIA technology
Schneider Electric accelerates the development and deployment of AI factories at scale with NVIDIA.AI factories – from environmental impact, tokens per watt, and liquid cooling to supplemental electricity from the grid, the need for 800 VDC Power, and talent shortages.
An AI factory is a purpose-built facility designed to run as a single, large compute cluster for training and deploying AI models. Unlike traditional data centers that support mixed workloads, AI factories optimize for maximum GPU throughput at scale. As NVIDIA’s Jensen Huang describes it, these facilities act like production plants: data and compute go in, and trained models and outputs come out.
As detailed in Schneider Electric's White Paper 110, the large scale and power needs of these GPU clusters, when training AI Models, draws peak power simultaneously. While traditional data centers were sized against average, diversified loads AI Factories must be sized for the synchronous peak a fundamentally different electrical design requirement that affects UPS systems, switchgear, and utility interconnection.
The increasing Thermal Design Power (TDP) of modern AI compute pushes rack densities well beyond 100 kW, far exceeding the thermal limits of air cooling. As documented in Schneider Electric's White Paper 133, direct-to-chip liquid cooling becomes necessary above approximately 20-30 kW per rack. Water's thermal conductivity and heat capacity make it orders of magnitude more effective than air at these densities. Critically, many current-generation AI servers ship with no fans at all, shifting the full thermal burden to facility-level liquid infrastructure.
As explained by our resident Schneider Electric chief AI advocate and expert, Steven Carlini, in this article, data centers are physical buildings and facilities which store IT infrastructure for specific applications and related services. They also serve as depositories for storing and managing data associated with these applications. On the other hand, AI factories are facilities which produce digital output from data centers.
AI factories manage the entire AI lifecycle, from model training and fine-tuning to performing inference. As AI models become more self-directed, optimized inference workloads are expected to run continuously based on new data, thereby producing additional value. While traditional data centers cannot support these higher power densities and intense thermal outputs, AI factories are using innovative reference designs to meet these operational requirements.
Digital twins reduce risk by allowing operators to simulate and validate infrastructure on a virtual model of the facility before and during deployment, and continuously during operation. Before build, they model power, cooling, and workload behavior to catch design flaws early, which would be expensive to remediate post-build (e.g., undersized cooling or limited electrical capacity).
During deployment, digital twins help compare options like prefab vs. traditional builds using real workload simulations. Rather than selecting technologies based on specification sheets, teams can validate actual behavior under simulated AI workloads before installation decisions are finalized. Once live, digital twins use real-time data to simulate the impact of a planned change (e.g., adding a new GPU cluster or rebalancing cooling zones) before executing it in production. They can also detect early signs of failure, giving teams sufficient lead time to intervene before a training run is interrupted.
AI factories cannot tolerate unplanned downtime; a single failure can stop a multi-week training job. Traditional maintenance models based on fixed schedules or reactive repairs are no longer sufficient. Best practice is condition-based maintenance (CBM), which uses real-time data to service equipment only when needed.
Schneider Electric’s EcoCare model applies this approach with 24/7 monitoring, AI-driven analytics, and expert support. Electrical systems are continuously monitored to manage peak loads on uninterruptible power supplies (UPSs) and switchgear, while liquid cooling systems are analyzed to detect risks like leaks or performance drops early. By shifting to predictive maintenance, operators can prevent failures before they happen and maintain continuous operation of critical AI infrastructure.
Read the sustainability cooling deal
Efficiency is measured as tokens per watt per dollar. Advanced power, liquid cooling, and automation can significantly improve efficiency per token, reducing both energy costs and environmental impact.
Get trusted insights and advice directly from our experienced experts.
Need help?
Product Selector
Quickly and easily find the right products and accessories for your applications.
Get a Quote
Start your sales inquiry online and en expert will connect with you.
Where to buy?
Easily find the nearest Schneider Electric distributor in your location.
Help Center
Find support resources for all your needs, in one place.