Not enough good American open models? Nvidia wants to help

"For many, enterprise AI adoption depends on the availability of high-quality open-weights models. Exposing sensitive customer data or hard-fought intellectual property to APIs so you can use closed models like ChatGPT is a non-starter. Outside of Chinese AI labs, the few open-weights models available today don't compare favorably to the proprietary models from the likes of OpenAI or Anthropic. This isn't just a problem for enterprise adoption; it's a roadblock to Nvidia's agentic AI vision that the GPU giant is keen to clear."

"When they launch, the models will be available in three sizes, Nano, Super, and Ultra, which weigh in at about 30, 100, and 500 billion parameters, respectively. In addition to the model weights, which will roll out on popular AI repos like Hugging Face over the next few months beginning with Nemotron 3 Nano this week, Nvidia has committed to releasing training data and the reinforcement learning environments used to create them, opening the door to highly customized versions of the models down the line."

Nvidia introduced three Nemotron open-weights LLMs—Nano (~30B), Super (~100B), and Ultra (~500B). The company will publish model weights on AI repositories like Hugging Face beginning with Nemotron 3 Nano, and will also release training datasets and reinforcement learning environments to enable customization. The models use a hybrid latent Mixture-of-Experts (MoE) architecture combining Mamba-2 and Transformer layers to reduce performance loss on long input sequences while preserving precise reasoning. Mamba-2 layers improve efficiency and token generation consistency for long prompts; transformers are retained to maintain reasoning fidelity and context retention during processing.

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