Nvidia's Jonathan Cohen discussed the rapid development of the Llama Nemotron models, which were created in just one to two months. This speed was largely possible due to the collaborative efforts of researchers across the company who sacrificed their GPU compute resources. Cohen also highlighted Nvidia's corporate culture that fosters prioritization of significant projects, encouraging managers to reallocate team members to contribute to new initiatives, thus enhancing overall progress in AI research and development.
In the world of AI research, the speed of development is limited in large part by available computing resources, according to Jonathan Cohen, Vice President of Applied Research at Nvidia.
Cohen led the team responsible for developing Nvidia's Llama Nemotron family of models. Released in March of this year, they represent the company's entry into the world of 'reasoning' AI systems.
The speed at which the models came together was remarkable, Cohen said, taking 'no more than one to two months.' He partially credits the efficiency of their development to other workers being willing to sacrifice their processing power.
Cohen also attributed the speed of development to Nvidia's company-wide culture of prioritizing major projects, regardless of current team goals.
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