NVIDIA's Vera Rubin is entering data centers soon, and the rise of Agentic AI also comes with a mammoth rise in costs.
NVIDIA Vera Rubin AI Data Centers Will Be Expensive To Build & Will Consume Lots of Power, A Single GigaWatt Installation Could Cost Roughly $50 Billion
The Vera Rubin era is upon us. The first systems are already being shipped to major cloud providers who are validating and testing them before bringing them up on scale. With the full volume production already going on, NVIDIA is eyeing an even bigger success than Blackwell.
But as these systems are deployed, Foxconn's Chairman, Young Liu, has stated that the costs are going to increase exponentially to set up Gigawatt-level datacenters. Vera Rubin will mark a huge disruption in the Agentic AI space, bringing record levels of compute performance that Blackwell couldn't even fathom to achieve.
As per Foxconn, AI data centers based on the NVIDIA Vera Rubin architecture will cost up to $47 Billion per 1GW installation. Each data center will hold up to 3,557 server racks, with each having a price tag of roughly $9.1M. These AI data centers will have an annual electric bill that touches $1.3 billion, and hardware depreciation is said to be 6x higher than the power bill.
A recent BoM breakdown from Morgan Stanley Research put the cost of VR200 NVL72 servers around $8 million.
At the same time, the construction cost is also very high. Young Liu cited an example: building a 1GW AIDC with Vera Rubin as its core would require a capital expenditure of up to US$47 billion and about 3,557 racks, while a single Vera Rubin rack costs US$9.1 million; moreover, the annual electricity cost of a 1GW AIDC is US$1.3 billion, and the hardware depreciation cost is six times the electricity cost.
We are already looking at multi-GW AI data centers being set up. By 2030, the global data center market is expected to hit $1.6 trillion with global compute consuming 174GW of power, more than 2x the power capacity required in 2024 (68GW). This means that through 2025-2030, an annual 18GW of new electricity capacity must be built to keep up with the growing demand.
The four primary customers who are demanding such compute include AI Model Developers, CSPs, Governments, and Enterprises. Most of these customers are still in the early AI empowerment stages, but future goals involve having AI native organizations where all processes operate with AI as the core, and humans only require setting and managing the goals & supervise the workflows and results.
For this purpose, Foxconn's chairman has proposed the idea of setting up "Taiwan-style" science and technology parks within the United States, mainly in Arizona and Texas. Efforts are already being made for these to take shape by the end of this year.
The Vera Rubin era signals a new frontier in AI, delivering unprecedented compute power for agentic systems, but at an extraordinary cost. As the world races toward AI-native organizations and multi-gigawatt deployments, the coming years will test how quickly power capacity, capital, and innovation can scale together. The winners will be those who master not just the technology, but the economics of building the AI infrastructure of tomorrow.
News Sources: Dan Nystedt , Commercial Times Taiwan , EC Taiwan
About the author: A Software Engineer by training and a PC enthusiast by passion, Hassan Mujtaba serves as Wccftech's Senior Editor for hardware section. With years of experience in the industry, he specializes in deep-dive technical analysis of next-generation CPU and GPU architectures, motherboards, and cooling solutions. His work involves not only breaking news on upcoming technologies but also extensive hands-on reviews and benchmarking.
Follow Wccftech on Google to get more of our news coverage in your feeds.
