NVIDIA Nemotron 3 models and Cosmos: Open Platforms for Agentic and Physical AI

By Agustin Giovagnoli / January 5, 2026

Enterprises evaluating next-wave AI platforms have a new pair of open ecosystems to consider. NVIDIA announced the NVIDIA Nemotron 3 models and Cosmos, designed to accelerate agentic AI across industries and physical AI for robots, autonomous vehicles, and video analytics, with a strong emphasis on published weights, datasets, and transparent training techniques [1][2][3][4][5][6].

Key takeaways for business and technical leaders

• Open, publish-weight models and associated datasets aim to streamline reuse, retraining, and governance for enterprise AI programs [1][2][3].
• Scalable sizes (Nano, Super, Ultra) and a hybrid latent mixture-of-experts architecture target efficiency, accuracy, and multi-agent workflows from edge to cloud [1][2][3].
• Reinforcement learning libraries and guardrails support building specialized, reliable agents in digital and physical domains [1][3][4][5][6].
• Alignment with “sovereign AI” strategies helps organizations adapt or retrain models with region-specific data and regulations [1][3].
• Cosmos provides generative world foundation models, controllable simulations, and video data curation pipelines for physical AI use cases [4][5][6].

What Nemotron 3 brings: models, datasets, and architecture

NVIDIA’s Nemotron 3 family is positioned as an open foundation for agentic AI, publishing model weights, training datasets, and training techniques so teams can fine-tune or retrain on their own data under their governance frameworks [1][2][3]. The lineup includes Nano, Super, and Ultra scales to match workload constraints and deployment targets, enabling use from edge devices to cloud infrastructure [1][2][3].

Under the hood, the models use a hybrid latent mixture-of-experts architecture designed to improve efficiency and accuracy for large multi-agent workflows, advanced reasoning, code generation, multimodal understanding, information retrieval, and safety-focused applications [1][2][3]. NVIDIA also provides reinforcement learning libraries and datasets to help organizations build specialized agents and evaluate complex, long-horizon reasoning tasks, while supporting region-specific regulatory needs tied to sovereign AI [1][2][3].

NVIDIA Nemotron 3 models: how to choose scale

• Nemotron open model weights and datasets enable reproducibility and customization for enterprise contexts [1][2][3].
• Nano, Super, and Ultra tiers are intended to balance cost, latency, and capability across deployment scenarios, from on-device inference to cloud-scale agent orchestration [1][2][3].
• Teams should map application requirements—reasoning depth, tool use, multimodality, and safety guardrails—to the appropriate model scale and deployment environment [1][2][3].

Cosmos: building physical AI with generative worlds and RL

Cosmos targets robots, autonomous vehicles, and video-based applications by pairing generative world foundation models with accelerated pipelines for video data processing and curation [4][5][6]. The platform includes guardrails and components such as Cosmos-Transfer2.5 to create realistic, controllable simulations—useful for producing high-quality synthetic data at scale [4][5][6]. For training, Cosmos-RL reinforcement learning capabilities support scalable experimentation and policy optimization for embodied systems [4][5][6].

These capabilities are designed to let teams use Cosmos to generate synthetic training data for autonomous vehicles, robotics, and video analytics, narrowing the gap between simulation and deployment while maintaining governance over data and processes [4][5][6].

Top enterprise use cases across industries

• Multi-agent orchestration for customer operations and decision support, leveraging Nemotron’s reasoning, tool use, and information retrieval [1][2][3].
• Synthetic simulation for robotics and autonomy, using Cosmos generative worlds and Cosmos-RL to test, train, and validate at scale [4][5][6].
• Video analytics pipelines with accelerated curation and guardrails to improve data quality and safety [4][5][6].
• Region-specific deployments under sovereign AI policies by retraining on local datasets and regulations [1][3].

Deployment, retraining, and governance considerations

Enterprises can deploy from edge devices to cloud environments by selecting the appropriate Nemotron 3 scale and integrating with their MLOps stack [1][2][3]. Retraining workflows should pair Nemotron’s open weights and datasets with organizational data, ensuring alignment to security and compliance regimes. For regulated markets, sovereign AI datasets NVIDIA strategies support data locality and oversight throughout the lifecycle [1][3].

In physical AI contexts, Cosmos offers controllable synthetic environments and curated video pipelines to source and refine training data. Combining these with Cosmos-RL reinforcement learning can accelerate iteration while preserving safety and auditability [4][5][6].

Choosing the right model and tooling: decision checklist

• Business goals: Define agentic tasks, safety constraints, and KPIs for reasoning and automation [1][2][3].
• Model scale: Use Nemotron 3’s Nano, Super, or Ultra tiers to balance latency, cost, and accuracy 123].
• Architecture fit: Validate the hybrid latent mixture-of-experts architecture against multi-agent workflows and long-horizon tasks [1][2][3].
• Data strategy: Plan retraining on enterprise data and region-specific corpora; use Cosmos for synthetic data and video curation where needed [1][3][4][5][6].
• Training loop: Apply Cosmos-RL reinforcement learning for scalable experimentation in robotics and autonomy [4][5][6].
• Governance: Implement guardrails and documentation throughout datasets, simulations, and deployment [1][4][5][6].

Next steps for teams: pilot projects and resources

Start with a focused proof of concept: a domain-specific agent built on Nemotron 3, or a simulation-driven robotics workflow in Cosmos. Reference NVIDIA’s detailed materials and repositories for setup and evaluation, including the Nemotron announcement and the Cosmos GitHub. For strategic planning and tooling selection, you can also explore AI tools and playbooks. For official details, see NVIDIA’s release and product pages, including the Nemotron 3 debut and Cosmos documentation 123456]. You can also consult NVIDIA’s press announcement here: NVIDIA’s press release (external) [3].

Sources

[1] NVIDIA Nemotron – Foundation Models for Agentic AI
https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/

[2] NVIDIA introduces open Nemotron 3 models for agentic AI
https://www.engineering.com/nvidia-introduces-open-nemotron-3-models-for-agentic-ai/

[3] NVIDIA Debuts Nemotron 3 Family of Open Models
https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models

[4] How NVIDIA Cosmos Is Shaping AI Applications for the Physical World
https://www.elementera.com/ai-case-studies/how-nvidia-cosmos-is-shaping-ai-applications-for-the-physical-world

[5] NVIDIA Cosmos – GitHub
https://github.com/nvidia-cosmos

[6] What Is Nvidia Cosmos? | Built In
https://builtin.com/articles/nvidia-cosmos