The Vice President of Applied Research at Nvidia, Jonathan Cohen, has stated that the pace of AI research is largely determined by access to computing power.

“These days, the currency in any AI researcher is how many GPUs they get access to, and that’s no less true at Nvidia than at any other company,” Cohen said in an interview on Nvidia Developer.

As AI models become more complex and resource-intensive, the availability of high-performance computing infrastructure plays a crucial role.

Cohen headed the team that developed Nvidia’s Llama Nemotron models, launched in March. These models represent Nvidia’s first major step into building AI systems designed for advanced reasoning.

Cohen emphasized the remarkable speed with which the models were developed, noting that they came together in “no more than one to two months.”

He partly attributes the efficiency of their development to other workers’ willingness to contribute their processing power.

“So, there were a lot of researchers who very selflessly agreed to give up their compute so that we could get these Llama Nemotron models trained as quickly as we did,” he said.

He further linked the fast development to Nvidia’s culture of prioritizing key projects across the company, even when it requires adjusting the focus of existing team objectives.

“How do you have a team to do a thing you’ve never done before? Part of the corporate culture is — we call them a ‘swarm’ — where you identify, ‘This is something that’s important,'” he said. “And everyone, every manager who has people who might be able to contribute, thinks about, ‘Is this new thing more important than the current thing everyone on my team is doing?'”

If the manager has the capacity, they’ll “delegate” their direct reports to the new priority.

“Llama Nemotron ended up being a very cross-discipline, cross-team effort,” Cohen added. “We had people from across the whole company working together without any formal organizational structure.”

Cohen noted that the development of Llama Nemotron involved a series of sacrifices, both in computing power and personnel.

However, individuals were able to put aside personal interests for the greater good.

“It was really great to see, great leadership,” he said. “There were a lot of sacrifices that people made, a lot of very egoless decisions that brought it together, which is just awesome.”