Nvidia positions Nemotron 3 as an open infrastructure for building domain-specific AI agents that can cooperate, coordinate, and execute across large contexts and long time periods. Nemotron 3 uses a hybrid latent mixture-of-experts (MoE) architecture and comes in Nano, Super, and Ultra sizes to suit different application needs. Developers can build agents or applications without training a foundation model from scratch, and most training data plus reinforcement learning libraries are being released for public use. The Nano targets efficient retrieval and assistant tasks, the Super targets high-accuracy reasoning and multi-agent collaboration, and the Ultra targets large, complex reasoning applications.
"AI agents must be able to cooperate, coordinate, and execute across large contexts and long time periods, and this, says Nvidia, demands a new type of infrastructure, one that is open. The company says it has the answer with its new Nemotron 3 family of open models. Developers and engineers can use the new models to create domain-specific AI agents or applications without having to build a foundation model from scratch."
"Nemotron 3 features what Nvidia calls a "breakthrough hybrid latent mixture-of-experts (MoE) architecture". The model comes in three sizes: Nano: The smallest and most "compute-cost-efficient," intended for targeted, highly-efficient tasks like quick information retrieval, software debugging, content summarization, and AI assistant workflows. The 30-billion-parameter model activates 3 billion parameters at a time for speed and has a 1-million-token context window, allowing it to remember and connect information over multi-step tasks."
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