f you're wondering why AI chips leader Nvidia is now building open models that compete with the Chinese open-source champs, and even proprietary models from OpenAI and Anthropic, then you're not alone.

Last month, Nvidia launched Nemotron 3 Super, a 120-billion-parameter reasoning model that outperformed expectations in benchmarks. This is a mixture-of-experts model with a 1-million-token context window. In other words, it's a serious model made to compete with the frontier labs. Meanwhile, the company promised that a model 4x its size, to be called Nemotron 3 Ultra, is coming soon.

And because Nvidia opens the weights, datasets, and training recipes, it's among the most open models in the world, especially for a model of this capability. Some of the only models that could claim to be more open would be the ones from MBZUAI, which The Deep View covered in depth in January. But Nvidia's open models are far closer to full-stack openness than most of the open-source models, which only offer open-weight releases.

So why would the leading hardware company of the AI era make software that competes with its leading customers?

"We're not trying to control AI. We're trying to grow it," Bryan Catanzaro, VP of applied deep learning research at Nvidia, told The Deep View. "And so our incentives as a company, our business is aligned with open models and with supporting the ecosystem in a very direct way."

Kari Briski, VP of generative AI software at Nvidia, told The Deep View another perspective: "The model is the byproduct. It is not core to our business, which allows us to just open up the data, open up the recipes, open up everything."

If we break it down, there are three benefits Nvidia gets from making its own models:

1. Extreme hardware co-design: Making their own models allows Nvidia to optimize the heck out of their GPUs, CPUs and other hardware to run AI. They don't have to wait to get the latest models from the frontier labs to plan the next stage of optimizations.
2. Hedging against proprietary monopolies: If the frontier labs that need the latest and greatest hardware dwindle down to only a handful of players, then Nvidia could end up at their mercy. When you rely on a smaller number of customers for a large number of orders, those customers gain more and more control over your prices. They can demand lower prices because they know so much of your business depends on them.
3. Letting a thousands flowers (a.k.a. customers) bloom: By releasing open models that other hardware and software makers can use as a rapid on-ramp to build their own AI products and serve the various niches in the industry, Nvidia is powering up the ecosystem, helping companies with limited resources have models they can use to compete and potentially creating a lot more future customers when those companies succeed and grow.

"You don't want one person winning [because] then they decide all the rules. You need a big open ecosystem for everybody to come along," said Briski.

Our Deeper View

Nvidia's open model strategy makes perfect sense from the perspective of being an ecosystem catalyst. The more it eases the on-ramp for companies of all sizes to bring their AI products to market, even if they can't afford to develop their own models, the more the whole ecosystem grows. And since Nvidia powers 90% of the GPUs in the generative AI ecosystem, every increase in demand translates directly to Nvidia's bottom line right now.