TL;DR · 30-second read

The Short Version

Nvidia, the company whose chips run most of today’s artificial intelligence systems, says it will sell twice as many chips next year as it does this year.

That sounds like it means twice as many data centers, twice the electricity and twice the cooling. It may not. Nvidia’s chips include the one inside the Nintendo Switch 2, plus chips for laptops and robots, and the company does not say how many of each it sells.

The businesses that must supply the power and cooling need that breakdown before they can plan.

Nvidia chief executive Jensen Huang said on Thursday, September 17, that the company will double the number of chips it sells next year, CNBC reported. “I expect Nvidia to sell twice as many chips as this next year as we do this year,” Huang said at a summit on AI safety with King Charles III in Scotland, attended also by representatives of Google DeepMind, OpenAI and Anthropic.

The remark follows Nvidia’s forecast of 70% growth for the fiscal year ending January 2028, a figure CNBC put at about $673 billion. Nvidia does not disclose how many chips it sells in total; last fall Huang said the company had shipped 6 million Blackwell GPUs in four quarters.

Executive Summary

Huang’s forecast is a headline-friendly number: twice as many chips in a single year. For the companies that build and supply AI data centers, it reads as a demand signal for everything a chip needs to run: electricity, cooling, high-bandwidth memory and the networking that links processors together.

The number is less precise than it sounds. Nvidia sells far more than data center graphics processing units (GPUs, the accelerators that train and run AI models). Its lineup includes central processors, network switch chips, optical networking chips, laptop chips, Jetson modules for robots and cars, and the processor in Nintendo’s Switch 2. A doubling of total units says nothing on its own about how many high-power data center accelerators are in the mix.

That matters because infrastructure is planned in megawatts and racks, not in chip counts. The direction of Huang’s forecast is clear; the load it places on grids and cooling plants is not yet specified.

A Unit Count Is Not a Load Forecast

A data center’s electricity and cooling needs are set by the accelerators and servers installed in it. A Blackwell or Rubin GPU sits in a liquid-cooled rack drawing power continuously; a Switch 2 processor sits in a handheld console on battery. Both count as one chip in a total unit figure. Doubling a blended count could therefore mean data center shipments doubled, or rose less while consumer and embedded lines rose more, or any combination in between.

The people who must translate Huang’s number into physical capacity — utilities fielding interconnection requests (applications to connect new load to the grid), developers securing sites, cooling-equipment makers, and memory suppliers allocating production — plan against deliveries of data center systems. Huang’s statement does not give them that figure. It confirms that demand is rising; it does not tell them by how much in the category that consumes the power.

That is the practical risk on both sides. Suppliers who read “twice the chips” as “twice the megawatts” could overbuild if the growth skews toward low-power parts. Suppliers who discount the statement could fall short if the growth is concentrated in data center accelerators. Either error is expensive in a business where transformers, cooling plants and memory lines take long lead times to add.

The Revenue Forecast Tells a Different Story Than the Unit Forecast

Nvidia’s own dollar guidance offers a cross-check. The company expects 70% growth in the fiscal year ending January 2028, which CNBC put at about $673 billion. That fiscal year broadly overlaps calendar 2027, the “next year” Huang appears to be describing.

If the two forecasts cover comparable periods, units growing 100% while revenue grows 70% implies average revenue per chip falls. One reading is a mix shift toward lower-priced parts; another is that the two figures simply measure different things over slightly different windows. Either way, the gap between them is a reason not to treat the unit figure as a proxy for data center buildout. The revenue figure, heavily weighted to data center products, is the better, though still indirect, gauge of the infrastructure load ahead.

What Power, Cooling and Networking Suppliers Can Still Take From It

The statement is not empty. Huang tied it to broad demand — “almost every single country that we’re in, people want to invest in AI” — and it extends a run of forecasts that, CNBC noted, pushed analyst expectations up and point to continued growth over the next six quarters. Nvidia’s list of product lines also includes switch chips and optical networking chips, so growth in AI clusters carries a networking and fiber component alongside compute.

For power equipment makers, cooling vendors, memory producers and data center landlords, the sensible reading is directional: demand for AI capacity continues to grow, and at a pace Nvidia expects to exceed this year’s. The sizing — how many megawatts, how many racks, how much memory — still depends on figures Nvidia has not broken out.

Background

Nvidia began as a graphics-chip company and became the dominant supplier of the accelerators used to train and run AI models. Its data center GPU families, Blackwell and now Rubin, are deployed in dense, often liquid-cooled racks whose power and cooling needs have reshaped how data centers are designed and where they are built.

The company also sells a wide range of other semiconductors, from networking and optical chips that link AI clusters together to processors for laptops, robots, cars and Nintendo’s Switch 2. That breadth is why a company-wide chip count and a data center buildout forecast are not the same thing.

Sources

Source: Jensen Huang says Nvidia will sell twice as many chips next year (CNBC) — Huang’s forecast of doubled chip sales, made at an AI safety summit in Scotland.