AI factories need more than faster chips. As workloads scale, power, cooling, and controls must evolve together.
Artificial intelligence is transforming the data center from a system that processes information into one that produces intelligence. As AI models scale, workloads are becoming more dynamic, distributed, and demanding across compute, networking, power, cooling, and operations. This shift requires a new infrastructure model, not just faster chips or denser racks.
NVIDIA MGX: A foundation for scalable AI systems
NVIDIA MGX is helping define that model. As an open, modular reference architecture for accelerated computing, it enables the ecosystem to design and deploy AI systems faster, with greater flexibility and multi-generational compatibility. From single-node servers to rack-scale systems and AI factory deployments, MGX provides a common architectural foundation for Original Equipment Manufacturers (OEMs), Original Design Manufacturers (ODMs), and partners building the next generation of AI infrastructure.
Extending the MGX philosophy to infrastructure
MGX provides a critical foundation for this transition. Its modular design enables system-level standardization and reduces engineering friction as AI platforms evolve.
That same modular philosophy must extend into the infrastructure layer.
Vertiv is advancing this approach through:
- Repeatable infrastructure building blocks
- Defined system interfaces
- Digitally enabled design and simulation
- Integrated power and thermal architectures
- Lifecycle services that support deployment and operation at scale
Our work with NVIDIA on simulation-ready infrastructure for Vera Rubin DSX AI factory reference designs reflects this direction, enabling infrastructure to be configured, visualized, validated, and optimized earlier in the design process, before deployment risk becomes operational risk.
The economics of AI factories are changing
The economics of AI factories are increasingly measured by output, not inputs.
Customers are no longer asking only:
- How many megawatts can we secure?
- How many racks can we deploy?
They are asking:
- How quickly can capacity become operational?
- How reliably can it support high-density compute?
- How efficiently can power be converted into AI performance?
These questions place new demands on infrastructure performance, integration, and predictability.
Designing for dynamic AI workloads
As AI workloads evolve toward reasoning, agentic workflows, larger context windows, and more complex compute patterns, infrastructure must adapt to increasingly dynamic load profiles. Liquid cooling, advanced power architectures, digital orchestration, and lifecycle assurance are becoming foundational requirements. Compute, power, and thermal systems can no longer be planned separately. They must be engineered together as a unified system.
From industry momentum to scaled deployment
The MGX ecosystem provides a more repeatable path forward, allowing partners to innovate on a shared architecture while maintaining flexibility across workloads, deployment models, and future generations of AI systems. For Vertiv, this aligns with how AI factories are evolving: open, modular, scalable, and built around validated systems rather than isolated components.
At Computex, the NVIDIA AI Factory MGX Ecosystem demonstration highlights the momentum building across the industry and signals where the market is heading. The next phase of AI infrastructure will be defined by:
- Industrialized deployment
- Reduced integration risk
- Scalable performance in increasingly dense environments
Turning design into operational capacity
Vertiv is proud to be part of the MGX ecosystem and to contribute to the critical digital infrastructure expertise required to move AI factories from concept to capacity. As accelerated computing continues to evolve, our focus remains on helping customers deploy infrastructure that is:
- Faster to design
- More predictable to build
- More efficient to operate
- Ready for multiple generations of compute
AI factories will shape the next era of digital infrastructure. MGX defines the modular compute foundation.
Converged infrastructure for AI factories
For Vertiv, the implication is clear: as compute becomes more modular and scalable, the physical infrastructure that supports it must evolve in the same way. AI factories cannot be built as collections of independent systems. At rack scale and campus scale, power, thermal management, controls, deployment, and lifecycle services become tightly interdependent. Every interface and conversion stage—and even small thermal margins—directly impacts time to capacity, reliability, efficiency, and ultimately the amount of AI output a facility can generate from available power.
That is why Vertiv is focused on converged infrastructure for AI factories, integrating power, cooling, controls, modular systems, and services into a single engineered architecture. It is designed to reduce complexity, improve deployment repeatability, and support predictable operations from grid connection through chip-level cooling.
As AI infrastructure scales, the goal is not only to build faster, but to build with greater consistency, efficiency, and performance across the full power and thermal chain.
