Siemens and nVent have released a joint reference architecture purpose-built for NVIDIA AI data centers. The design pairs Siemens industrial-grade electrical and automation systems with nVent liquid cooling to address the steep energy and thermal demands of hyperscale AI workloads.
Addressing the Hyperscale AI Challenge
AI training and inference at scale drive rack-level power densities far beyond traditional data center norms. Hyperscale designs targeting 100 MW capacity and deployments such as NVIDIA DGX SuperPOD with DGX GB200 systems require tightly integrated power distribution and thermal management to remain efficient, reliable, and scalable. Operators face urgent needs to limit energy per model output while keeping compute available at short notice.
A Blueprint for Efficiency and Scale
The reference architecture combines Siemens electrical distribution, automation, and control with nVent direct liquid cooling hardware and manifolding. Siemens supplies redundant power distribution, industrial controls and monitoring that map to data center busways, PDUs, and protection systems. nVent provides rack- and GPU-level liquid cooling that reduces coolant temperatures and limits thermal throttling. Together the stack reduces cooling energy, shortens deployment timelines, and increases achievable tokens-per-watt for AI workloads.
Key operator benefits include modular deployment for rapid scaling, fault-tolerant electrical topologies, precision thermal control to avoid GPU de-rating, and integrated monitoring for predictive maintenance. The design supports concentrated compute clusters like DGX SuperPOD while enabling incremental capacity growth up to 100 MW footprints.
Impact for Future AI Infrastructure
By aligning electrical architecture with advanced liquid cooling, the Siemens and nVent reference offers a practical path to lower the energy intensity of next-generation AI. Lower energy per inference or training token reduces operating cost and carbon intensity for large AI facilities. For investors, engineers, and operators, this architecture signals a shift toward purpose-built energy systems that balance performance, resilience, and sustainability as AI scales.
Operators evaluating large-scale NVIDIA deployments should consider reference architectures that co-design power and cooling to meet the energy and reliability demands of hyperscale AI.
