Nvidia (NVDA) is the world’s leading AI chipmaker. Its GPUs are found in data centers operated by the likes of Amazon (AMZN), Google (GOOG, GOOGL), Meta (META), Microsoft (MSFT), Oracle (ORCL), and xAI, generating billions in revenue.
While the company’s relationships with Big Tech are undoubtedly important, its connections to research universities and colleges across the country and world are no less significant to Nvidia’s long-term success.
That’s because, as university researchers come up with new techniques for using GPUs, Nvidia, and its competitors, can take advantage of them to develop new technologies.
After all, Nvidia’s leadership role in AI kicked off in 2012 when researchers at the University of Toronto used the company’s GPUs to develop their image recognition tool, AlexNet. This opened the door to how Nvidia’s chips could be used to power AI applications and helped to turn Nvidia into what it is today.
“One of [CEO Jensen Huang’s] most brilliant moves early on was to start to work with researchers on the very beginnings of AI,” TECHnalysis chief analyst Bob O’Donnell explained.
Nvidia CEO Jensen Huang delivers a speech during the Computex 2025 exhibition in Taipei, Taiwan, on May 19. (AP Photo/Chiang Ying-ying, File) ·ASSOCIATED PRESS
“He figured out early on that it was going to take a lot of work and research to do this,” O’Donnell added. “So he jumped in and wholeheartedly supported academic research into AI, and all of that has paid off in spades. And so he’s obviously continuing that approach, because he knows that that’s where a lot of the most interesting developments are happening.”
Naturally, those same universities benefit in their own way, creating a kind of flywheel that continues to spin faster each day.
Nvidia says it works with universities in three major ways: through infrastructure, the actual hardware necessary to run AI research and programs; its software libraries, like CUDA, which help researchers and students develop new AI applications and use cases; and hackathons, grants, and joint research projects.
The University of Florida received donations from Nvidia, as well as Nvidia co-founder and University of Florida alumnus Chris Malachowsky, to help deploy its HiPerGator supercomputer in 2020. The school was the first in the US to use Nvidia’s A100 and Blackwell chips and developed a program that allows all students to gain access to the high-powered system.
“We launched an initiative called AI Across the Curriculum, which meant that every student at the University of Florida, all 60,000 some students … will have the opportunity to learn about AI, work with AI at different levels of complexity, from basic to as advanced as building digital twins,” University of Florida vice president and chief information officer Elias G. Eldayrie explained.
“So HiPerGator now supports about 67% of the university research portfolio. And that research portfolio is about $1.2 billion,” he added.
Nvidia CEO Jensen Huang addresses participants at the keynote of CES 2025 in Las Vegas on Jan. 6, 2025. (Artur Widak/NurPhoto via Getty Images) ·NurPhoto via Getty Images
So far, Eldayrie said, the university is using AI in healthcare, agriculture, and building maintenance. In healthcare, the university built a digital twin of an emergency room to simulate the ER experience, including everything from managing the room temperature to how many times a patient gets interrupted in an effort to improve surgical outcomes.
In agriculture, the university is analyzing data from drones to determine the best treatment for specific crops rather than spraying entire fields. The school has also built a digital twin of the city of Jacksonville to simulate buildings and how best to maintain them.
The Georgia Institute of Technology is building out its new Nvidia-powered Nexus supercomputer that the school says will allow it to perform research on large global problems.
“If you think about curing cancer. It’s just very dependent on complicated histories of people and their environment, and so just keeping track of all that stuff is a really challenging problem,” Georgia Tech executive vice president for research Tim Lieuwen explained.
“Or … keeping our country safe; radar systems to detect incoming missiles and deploy systems to take them out,” he added. “Those are really complicated problems where AI can really help. Taking the best in robotics, in AI, and bringing really exciting, high value jobs back to the United States in manufacturing. So these are all really interesting applications, and applications where this [high-performance computer] resource is going to help Georgia Tech researcher … move the needle.”
The State University of New York’s University at Albany received millions in funds from the 2022 state budget to set up its Nvidia-powered supercomputer facility. The goal is to perform research on cybersecurity, weather modeling and climate prediction, and fine-tuning large-scale AI models.
“It is really being used heavily,” University at Albany VP for research and economic development Thenkurussi Kesavadas explained.
“I will tell you that faculty members are using it for research, but also students … are using that in their advanced classes on AI, and doctoral students and PhD students are using it for their own research in collaboration with faculty members,” Kesavadas added.
To be sure, universities also lean on Nvidia rival Advanced Micro Devices (AMD) to power their supercomputers, as well as Intel (INTC) CPUs. University supercomputers are also far smaller than the warehouse-sized systems cloud computing companies like Amazon, Microsoft, and Google operate.
Nvidia also benefits from working with universities. In addition to breakthroughs like AlexNet, Nvidia’s involvement with research schools ensures a steady stream of new students familiar and comfortable with using its AI software and hardware.
“[Huang] gets a whole bunch of grad students who get trained in their technology and then are ready to get hired and continue that work at Nvidia,” O’Donnell said.
“It’s not just the product benefits, it’s the pipeline. Because, look, we’re still in an era when … there’s not enough of these [AI experts] out there,” he added.
The company also gets a healthy dose of cachet whenever researchers announce new AI advancements they’ve reached using Nvidia’s chips.
While that might not directly benefit the company’s bottom line, it serves to continue to build the brand’s reputation as an AI leader.
For now, Nvidia continues to be the go-to choice for AI chips, followed by AMD. But that may not remain the case in the future as the technology continues to evolve and companies like Google and Amazon build their own specialized AI processors.
But at the moment, Nvidia doesn’t appear to be losing its grip on the AI market or the university space. And if it can hold on to that, the flywheel will keep spinning.
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Email Daniel Howley at dhowley@yahoofinance.com. Follow him on X/Twitter at @DanielHowley.
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