Nvidia commits $26B to open-weight AI models, releases Nemotron 3 Super

Nvidia is investing $26 billion over the next five years to develop open-weight AI models, according to 2025 financial filings confirmed by executives. This strategic move positions Nvidia to compete directly with frontier AI labs like OpenAI and DeepSeek, while reinforcing its hardware dominance since the models are tuned for Nvidia's chips.

Nemotron 3 Super release details

On Wednesday, Nvidia released Nemotron 3 Super, its most capable open-weight model to date. The model has 128 billion parameters, making it roughly equivalent to the largest version of OpenAI's GPT-OSS. Nvidia claims it outperforms GPT-OSS and other models across several benchmarks:

• Scored 37 on the Artificial Intelligence Index (GPT-OSS scored 33)
• Ranks number one on PinchBench, a new benchmark assessing model ability to control OpenClaw
• Several Chinese models scored higher on the AI Index

Technical innovations and training

Nvidia introduced architectural and training techniques that improve reasoning abilities, long-context handling, and responsiveness to reinforcement learning. The company recently finished pretraining a 550-billion-parameter model and has released specialized models for robotics, climate modeling, and protein folding.

Open model landscape context

Meta was first to release an open model (Llama in 2023) but may not make future models fully open. OpenAI's GPT-OSS is inferior to proprietary offerings and not well-suited to modification. Top US models from OpenAI, Anthropic, and Google are cloud/chat-only. By contrast, Chinese models from DeepSeek, Alibaba, Moonshot AI, Z.ai, and MiniMax release weights openly and for free, leading many startups and researchers to build on them.

Strategic implications

Nvidia's open models help test and improve not just chips but also super-computer-scale datacenters, storage, networking, and hardware architecture. The investment aims to counter the rise of Chinese open models that could erode Nvidia's position if they demonstrate dramatic improvements on rival hardware. DeepSeek's January 2025 release used more efficient training approaches that reduced costs significantly.