NVIDIA’s Nemotron 3 Pushes Open Multi Agent AI Into The Mainstream

NVIDIA has released a new family of open artificial intelligence models dubbed Nemotron 3, aimed at developers building multi-agent AI systems, an area gaining attention as companies move beyond single chatbots to more complex, collaborative AI workflows.

The Nemotron 3 lineup includes three model sizes, Nano, Super, and Ultra, and is built on a hybrid mixture-of-experts (MoE) architecture. 

The architecture is designed to cut inference costs and improve efficiency while allowing multiple AI agents to work together without losing context or reliability, two common challenges in large-scale deployments.

The launch comes as companies are experimenting with agentic AI systems that divide tasks across specialized models rather than relying on a single, general-purpose one. 

While such systems promise better performance, they also introduce problems such as communication overhead between agents, rising compute costs, and limited transparency into how decisions are made. 

NVIDIA positions Nemotron 3 as a response to these constraints, particularly for organizations that want more control over how models behave and are trained.

Nemotron 3 Nano, available immediately, is a 30-billion-parameter model that activates only a small fraction of its parameters at a time, making it suitable for tasks such as summarization, software debugging, and information retrieval. 

NVIDIA says the model improves token throughput compared with earlier versions and supports a large context window, allowing it to handle longer, multi-step workflows. 

Independent benchmarking group Artificial Analysis has ranked it highly among open models of similar size for efficiency and accuracy.

The larger Nemotron 3 Super and Ultra models, designed for complex reasoning and multi-agent coordination, are expected in the first half of 2026. 

These models are trained using a low-precision format on NVIDIA’s Blackwell architecture, which the company says reduces memory requirements and speeds up training without significant loss in accuracy.

Alongside the models, NVIDIA has released datasets and open-source tools intended to help developers customize and evaluate AI agents. These include large-scale training datasets, reinforcement learning libraries, and safety evaluation tools, all hosted on open platforms such as GitHub and Hugging Face.

Several tech and consulting firms, including ServiceNow, Perplexity, and Siemens, are testing or integrating Nemotron models into their workflows, spanning areas such as cybersecurity, manufacturing, and enterprise automation. 

More broadly, the release aligns with growing interest in “sovereign AI,” where governments and organizations seek models that can be adapted to local data, regulations, and operational needs rather than relying entirely on closed, proprietary systems.