Delivering agile AI infrastructure with precision

We are building the AI supply chain for continuous compute to keep up with the pace of demand and innovation.

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Roughly every twelve months, a new generation of GPUs leapfrogs the last in performance and power demands. With each release, the previous generation can lose 20 to 30 percent of its value within a year. In this environment, the challenge is no longer how fast organizations can build AI infrastructure but how adaptable their infrastructure can be in keeping pace with innovation.

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At the same time that technology cycles accelerate, customer demands are evolving faster than ever — shaped by shifting AI workloads, the rise of new AI models, advancing sustainability goals, and emerging regulatory frameworks. AI infrastructure must evolve with it without interrupting production or performance. 

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A trending metric that captures this adaptability is “time-to-first token,” which measures how quickly an organization can turn its AI investment into productive compute, delivering the first output, or “token,” from a running model. Reducing that interval isn’t merely about faster deployment; it’s about creating systems that can pivot, refresh, and reconfigure as innovation advances.

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In the new era of AI, success will hinge on continuous adaptation and the ability to evolve with each technological breakthrough, scale with sustainability in mind, and respond dynamically to ever-changing demands. AI-native organizations that engineer for agility from the start are best positioned to sustain performance, efficiency, and competitive advantage in this rapidly shifting landscape.

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The AI data center challenge: A moving target

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As organizations and governments expand their AI ambitions, the complexity of the underlying infrastructure increases dramatically. Power demands for high-density deployments that once averaged between 7 and 10 kilowatts per rack are now climbing toward 150 kilowatts, with predictions for 1 megawatt plus racks coming soon. At the same time, customer demands are growing and shifting faster than ever, driven by changing AI workloads and newer AI models, evolving sustainability targets, and emerging regulatory frameworks. Customers increasingly expect greater ease, usability, and convenience. This is forcing new approaches not only to cooling, power distribution, and redundancy but also to how infrastructure itself is designed and managed over time. 

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Many traditional data center providers still design facilities from the outside in, focusing on buildings rather than outcomes. This approach historically ties up capital in fixed structures that risk becoming obsolete before the next generation of GPUs, or the next customer change request, arrives. In contrast, infrastructure engineered for AI could be designed from the inside out, with the flexibility to accommodate new technologies, topologies, and use cases without costly and slow rework or delay.

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The value of AI-first infrastructure lies not just in how quickly it can deliver usable, efficient, and sustainable compute capacity, but in how adaptable it remains as customer needs evolve. Whether that means integrating new AI accelerators, accommodating shifts in data sovereignty, or rebalancing workloads across regions, the infrastructure must be built to respond dynamically.

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Over-engineering for tomorrow’s hardware increases cost, extends deployment timelines, and often leaves expensive capacity idle while technology moves on. In a market defined by rapid innovation, that means lost revenue and delayed advantage.

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Flexibility is now essential, yet many infrastructures remain locked into rigid, single-vendor designs that can’t evolve or scale to meet the pace of AI innovation and customers’ changing needs. It’s time to rethink how to deliver continuous compute.

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The approach: digital twins + modular design

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Every new GPU generation brings different power and cooling profiles, so infrastructure must adapt with agility and precision, not just speed. 

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This is where digital twins combined with modular builds play a transformative role. Digital twins allow organizations to model entire sites, from power and cooling to compute and network layers, in a unified virtual environment and serve as the foundation for modular AI infrastructure.

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Before any hardware is installed, teams can visualize and test how prefabricated modules will perform, interact, and scale. This virtual-first approach eliminates guesswork, reduces design cycles, and ensures every component is optimized for performance and efficiency before it ever ships to the field. For example, a digital twin-driven workflow can enable engineers to simulate how new GPU architectures will impact thermals, power draw, and rack density, then adjust module designs virtually before physical upgrades occur. 

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Building prefabricated self-contained units that integrate power, cooling, and compute can then be installed, connected, and commissioned rapidly, either as components of larger AI giga-factories or as standalone systems. Combined with digital twins, these modules can be validated, optimized, and deployed with confidence giving organizations access to live GPU capacity faster than ever.

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This modular architecture can dramatically shorten time-to-first token because the electrical, hydraulic, and telemetry systems are pre-engineered and performance-tested in simulation. Once deployed, the digital twin continues to serve as a living operational model that can monitor sensor data, forecasting performance, and orchestrate workloads based on real-time conditions such as thermal headroom, power availability, and latency. 

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The result is improved utilization, lower cost per workload, and a more resilient system that can adapt to change automatically.

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The shift to modular design is gaining momentum. A prime example is NVIDIA’s DSX Omniverse Blueprint, a reference architecture for data centers introduced by CEO Jensen Huang at GTC Washington, which emphasizes modular solutions for flexibility and scalability.

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By combining this modular architecture with Nscale’s digital twin technology, the AI factory becomes more than a facility. It becomes a living, adaptive system capable of evolving in step with innovation and at scale.

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Re-engineering the infrastructure

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Delivering next-generation AI infrastructure is being shaped on multiple fronts, from how systems are built to how they are operated and orchestrated, redefining efficiency, scalability, and sustainability across the data center landscape. 

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As AI tokenomics emerges as a framework for optimizing the use of computing power, data, and models, it is redefining how infrastructure must be designed. The efficiency of every watt, workload, and component will be scrutinized more closely than ever as unprecedented capital continues to flow into AI facilities.

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100% renewable cooling systems

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A cornerstone of performance efficiency is the cooling systems for giga-factories. With AI workloads intensifying, traditional air-based systems are reaching their limits. New liquid-based approaches deliver far greater thermal capacity while using less energy overall. These systems circulate coolant through precisely engineered channels to remove heat directly from components, reducing fan power, improving stability, and extending hardware lifespan. Better still is when there is an abundant source of renewable energy.

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For example, the Nscale facility in Glomfjord uses 100% renewable energy and a closed-loop cooling system. The cooling system begins with cold water drawn from the nearby fjord at just 6 to 9°C, absorbs heat from the data center and exits at around 34°C. This also means our site has substantial thermal headroom, as NVIDIA’s design expectations allow them to operate at temperatures of up to 45 degrees Celsius, and we do not expend significant energy maintaining temperatures, getting the best economics.

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Agile and modular construction

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Innovation in construction materials and modular design is also transforming how facilities are deployed and maintained. Lightweight, high-strength composites such as fiber-reinforced polymers (FRP) could replace traditional steel structures, reducing embodied carbon and making assembly and disassembly dramatically faster. These materials can be molded into functional components, such as integrated cable trays or structural panels, eliminating the need for secondary fabrication and allowing entire modules to be reused or repurposed as technology evolves. That innovation can then spread to how fast those components can be assembled, such as dropping a ceiling space that houses cables to lay out cables in a convenient manner on the ground prior to locking into place and raising the tray afterwards into place.

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These advances and opportunities to streamline operations point toward a new standard for AI infrastructure: one that operates with greater efficiency and agility, requires less power and time to build, and can rapidly respond to evolving customer needs with minimal waste. 

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The refresh cycle

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Refreshing infrastructure in a traditional data center can be slow, disruptive, and capital-intensive. Each new generation of AI hardware with denser GPUs, new interconnects, or advanced cooling systems, demands reconfiguration that often takes longer than the original fit-out, leaving valuable capacity offline.

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In contrast, AI-native modular infrastructure is engineered for continuous evolution. These facilities treat compute as an industrial output, similar to a “factory” that produces intelligence or tokens and are designed never to stop production. Modular blocks of compute, power, and cooling can be replaced or upgraded independently, allowing the site to stay online even during refresh cycles.

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Designing for this level of adaptability changes every architectural decision. Buildings feature wide access points, removable walls, and built-in crane systems for quick hardware swaps. Cooling systems use looped circuits with isolation valves, so sections can be retooled without shutting down the entire site.

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By anticipating generation-to-generation upgrades, modular AI data centers become future-ready production environments, built to deliver compute continuously while keeping pace with the rapid refresh rhythm of AI innovation.

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Customer-led AI innovation

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AI provisioning is about creating a repeatable, modular supply chain for intelligence itself. The future of AI infrastructure belongs to those who can adapt continuously by evolving with each generation of technology, scaling sustainably, and responding fluidly to the changing demands of innovation.