NVIDIA invests $26 billion to develop AI models, directly challenging OpenAI

NVIDIA Announces $26 Billion Investment Over the Next Five Years to Develop Open-Source AI Large Models

This global leading AI chip manufacturer is officially transforming into a frontier model laboratory, directly challenging the market positions of OpenAI, Anthropic, and DeepSeek.

According to the Wired article obtained on the 11th, which includes NVIDIA’s 2025 financial documents and executive interviews, the company’s management has confirmed this substantial investment. Meanwhile, NVIDIA released its most powerful open-source model to date, Nemotron 3 Super, claiming to outperform OpenAI’s open-source GPT-OSS model on multiple benchmarks.

This move’s impact on the market cannot be underestimated. NVIDIA’s chips have always been the industry standard for training large AI models, and its open-source models are specifically optimized for its hardware, further consolidating its dominance in AI computing power.

From a broader perspective, this investment marks a profound strategic shift for NVIDIA—from a pure hardware and software stack provider to a full-stack AI company capable of competing directly with top AI labs.

Nemotron 3 Super: Performance Metrics Rival Top Models

NVIDIA’s latest release, Nemotron 3 Super, has 128 billion parameters, roughly comparable in size to the largest version of OpenAI’s GPT-OSS. NVIDIA claims that in the Artificial Intelligence Index’s comprehensive score, Nemotron 3 Super scored 37 points, while GPT-OSS scored only 33—although the company also admits that some Chinese models scored higher.

Additionally, NVIDIA stated that Nemotron 3 Super participated in a new benchmark called PinchBench, which specifically evaluates a model’s control over OpenClaw. Nemotron 3 Super ranked first in this test.

Technically, NVIDIA disclosed several innovative methods used to train this model, covering architecture and training techniques that enhance inference capabilities, long-context processing, and reinforcement learning responsiveness. Bryan Catanzaro, Vice President of Deep Learning Research at NVIDIA, said, “NVIDIA is placing much greater emphasis on open-source model development than ever before, and we are making significant progress.”

Catanzaro also revealed that NVIDIA recently completed pre-training a 550 billion parameter model. Since releasing its first Nemotron model in November 2023, NVIDIA has gradually launched specialized models for vertical fields such as robotics, climate modeling, and protein folding.

Hardware and Model Dual-Drive Strategic Logic

NVIDIA’s move is not just about model competition but also a strategic layout deeply tied to its hardware roadmap. Kari Briski, Vice President of Generative AI Software Business at NVIDIA, stated that future AI models will not only serve chip development but also optimize the overall architecture of supercomputing data centers. “We build these models to extend our systems, not only testing computational power but also storage and networking, thereby shaping our hardware architecture roadmap,” she said.

The open-source strategy also has longer-term commercial significance for NVIDIA. When releasing models, NVIDIA makes weights and technical details public, facilitating startups and researchers to modify and innovate based on its technology. This helps form a developer network around NVIDIA’s hardware ecosystem, further strengthening the market stickiness of its chips.

Catanzaro said, “Helping the ecosystem grow aligns with our interests.” He joined NVIDIA in 2011 and led the company’s historic transition from gaming graphics cards to AI chips.

Industry Experts Highly Praise Its Strategic Significance

The research community has responded positively to NVIDIA’s deployment. Nathan Lambert, AI researcher at the Allen Institute for AI (AI2) and head of the American True Open-Source Model (ATOM) project, called himself a “big supporter of Nemotron” and urged the U.S. government to also fund open-source models.

Andy Konwinski, founder of the nonprofit Laude Institute focused on promoting AI openness and a computer scientist, characterized NVIDIA’s investment as a milestone signal. “They are at the forefront of many open and closed AI projects,” Konwinski said, “and this is an unprecedented statement of their commitment to openness.”

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