Nvidia plans to invest $26 billion over five years to develop open-source AI models, marking a strategic shift from a chipmaker to a frontier AI lab that can compete with companies like OpenAI. The company simultaneously released its most capable open-weight model to date, Nemotron 3 Super, which it claims outperforms rivals on several benchmarks.
The article details the competitive open-source AI landscape, noting that while U.S. leaders often keep their best models proprietary, many top Chinese models are openly released, attracting global developers. Nvidia executives state their open model development serves to advance the broader AI ecosystem and provides critical data for improving the company's own hardware and data center systems.
The main topics covered are: Nvidia's massive investment in open-source AI, the release and capabilities of the Nemotron 3 Super model, the competitive state of open-source AI models globally, and the strategic benefits to Nvidia's hardware business.
Nvidia will spend $26 billion over the next five years to build open source artificial intelligence models, according to a 2025 financial filing. Executives confirmed the news, which has not been previously reported, in interviews with WIRED.
The sizable investment could see Nvidia evolve from a chipmaker with an impressive software stack into a bona fide frontier lab capable of competing with OpenAI and DeepSeek. It’s a strategic move that could further entrench Nvidia’s place as the AI world’s leading chip manufacturer, since the models are tuned to the company’s hardware.
Open source models are ones where the weights or the parameters that determine a model’s behavior are released publicly—sometimes with the details of its architecture and training. This allows anyone to download and run it on their own machine or the cloud. In Nvidia’s case, the company also reveals the technical innovations involved in building and training its models, making it easier for startups and researchers to modify and build upon the company’s innovations.
On Wednesday, Nvidia also released Nemotron 3 Super, its most capable open-weight AI model to date. The new model has 128 billion parameters (a measure of the model’s size and complexity), making it roughly equivalent to the largest version of OpenAI’s GPT-OSS, though the company claims it outperforms GPT-OSS and other models across several benchmarks.
Specifically, Nvidia claims Nemotron 3 Super received a score of 37 on the Artificial Intelligence Index, which scores models across 10 different benchmarks. GPT-OSS scored 33—but several Chinese models scored higher. Nvidia says Nemotron 3 Super was secretly tested on PinchBench, a new benchmark that assesses a model’s ability to control OpenClaw, and ranks number one on that test.
Nvidia also introduced a number of technical tricks that it used to train Nemotron 3. These include architectural and training techniques that improve the model’s reasoning abilities, long-context handling, and responsiveness to reinforcement learning.
“Nvidia is taking open model development much more seriously,” says Bryan Catanzaro, VP of applied deep learning research at Nvidia. “And we are making a lot of progress.”
Open Frontier
Meta was the first big AI company to release an open model, Llama, in 2023. CEO Mark Zuckerberg recently rebooted the company’s AI efforts, however, and signaled that it might not make future models fully open. OpenAI offers an open-weight model, called GPT-oss, but it is inferior to the company’s best proprietary offerings, not well-suited to modification.
The best US models, from OpenAI, Anthropic, and Google, can be accessed only through the cloud or via a chat interface. By contrast, the weights for many top Chinese models, from DeepSeek, Alibaba, Moonshot AI, Z.ai and MiniMax are released openly and for free. As a result, many startups and researchers around the world are currently building on top of Chinese models.
“It's in our interest to help the ecosystem develop,” says Catanzaro, who joined Nvidia in 2011 and helped spearhead the company’s shift from making graphics cards for gaming to making silicon for AI. Nvidia released the first Nemotron model in November 2023. He adds that Nvidia recently finished pretraining a 550-billion-parameter model. (Pretraining involves feeding huge quantities of data into a model spread across vast numbers of specialized chips running in parallel.) Nvidia has since released a range of models specialized for use in areas like robotics, climate modelling, and protein folding.
Kari Briski, VP of generative AI software for enterprise, says Nvidia’s future AI models will help the company improve not just its chips but also the super-computer-scale datacenters it builds. “We build it to stretch our systems and test not just the compute but also the storage and networking, and to kind of build out our hardware architecture roadmap,” she says.
Releasing models openly may have long-term strategic benefits for Nvidia, too. The company’s chips remain the gold standard for training large AI models, with customers spending billions to acquire the company’s hardware for their datacenters. But the rise of Chinese open models might at some point erode Nvidia’s position if those models were to demonstrate dramatic improvements on rival hardware.
In January 2025, DeepSeek released a cutting-edge open model using a more efficient approach that made its training far cheaper. But a variety of other Chinese models from big companies like Alibaba, as well as startups like Moonshot AI, Z.ai, and MiniMax, have also become popular in the West. Alibaba’s model Qwen, which is easy to use and modify and is well maintained, is widely used by researchers and startups.
A new DeepSeek model, expected to be released soon, is widely rumored to have been trained exclusively on chips made by the Chinese company Huawei, which is subject to US government sanctions. If true, the release could prompt more startups and researchers to try Huawei’s hardware, particularly in China.
In this respect, Nvidia may help shape AI competition between the US and China by providing a US-made alternative to open-weight Chinese models.
“We're an American company, but we work with companies across the world,” Catanzaro says. “It's in our interest to make the ecosystem diverse and strong everywhere.”
Some industry experts have warned that seeing open innovation shift to the other side of the world could be bad for the US in the long run.
“I'm a huge Nemotron fan,” says Nathan Lambert, an AI researcher at the Allen Institute for AI (Ai2) who leads the ATOM (American Truly Open Models) Project. Lambert adds that the US government should also fund open models.
Andy Konwinski, a computer scientist and entrepreneur who leads the Laude Institute, a nonprofit focused on promoting openness in AI, says Nvidia’s investment is highly significant because of its position at the nexus of AI research. “They sit at the front of so many open and closed AI efforts,” Konwinski says. “This is an unprecedented signal of their belief in openness.”
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