Nvidia CEO Jensen Huang mentioned final 12 months that we at the moment are coming into the age of bodily AI. Whereas the corporate continues to supply LLMs for software program use circumstances, Nvidia is more and more positioning itself as a supplier of AI fashions for totally AI-powered methods — together with agentic AI within the bodily world.
At CES 2026, Nvidia introduced a slate of recent fashions designed to push AI brokers past chat interfaces and into bodily environments.
Nvidia launched Cosmos Cause 2, the newest model of its vision-language mannequin designed for embodied reasoning. Cosmos Cause 1, launched final 12 months, launched a two-dimensional ontology for embodied reasoning and at present leads Hugging Face’s bodily reasoning for video leaderboard.
Cosmos Cause 2 builds on the identical ontology whereas giving enterprises extra flexibility to customise purposes and enabling bodily brokers to plan their subsequent actions, just like how software-based brokers purpose by means of digital workflows.
Nvidia additionally launched a brand new model of Cosmos Switch, a mannequin that lets builders generate coaching simulations for robots.
Different vision-language fashions, equivalent to Google’s PaliGemma and Pixtral Giant from Mistral, can course of visible inputs, however not all commercially obtainable VLMs assist reasoning.
“Robotics is at an inflection point. We are moving from specialist robots limited to single tasks to generalist specialist systems,” mentioned Kari Briski, Nvidia vice chairman for generative AI software program, in a briefing with reporters. She was referring to robots that mix broad foundational data with deep task-specific expertise. “These new robots combine broad fundamental knowledge with deep proficiency and complex tasks.”
She added that Cosmos Cause 2 “enhances the reasoning capabilities that robots need to navigate the unpredictable physical world.”
Shifting to bodily brokers
Briski famous that Nvidia’s roadmap follows “the same pattern of assets across all of our open models.”
“In building specialized AI agents, a digital workforce, or the physical embodiment of AI in robots and autonomous vehicles, more than just the model is needed,” Briski mentioned. “First, the AI needs the compute resources to train, simulate the world around it. Data is the fuel for AI to learn and improve and we contribute to the world's largest collection of open and diverse datasets, going beyond just opening the weights of the models. The open libraries and training scripts give developers the tools to purpose-build AI for their applications, and we publish blueprints and examples to help deploy AI as systems of models.”
The corporate now has open fashions particularly for bodily AI in Cosmos, robotics, with the open-reasoning vision-language-action (VLA) mannequin Gr00t and its Nemotron fashions for agentic AI.
Nvidia is making the case that open fashions throughout totally different branches of AI type a shared enterprise ecosystem that feeds information, coaching, and reasoning to brokers in each the digital and bodily worlds.
Additions to the Nemotron household
Briski mentioned Nvidia plans to proceed increasing its open fashions, together with its Nemotron household, past reasoning to incorporate a brand new RAG and embeddings mannequin to make info extra available to brokers. The corporate launched Nemotron 3, the newest model of its agentic reasoning fashions, in December.
Nvidia introduced three new additions to the Nemotron household: Nemotron Speech, Nemotron RAG and Nemotron Security.
In a weblog submit, Nvidia mentioned Nemotron Speech delivers “real-time low-latency speech recognition for live captions and speech AI applications” and is 10 instances quicker than different speech fashions.
Nemotron RAG is technically comprised of two fashions: an embedding mannequin and a rerank mannequin, each of which may perceive photographs to offer extra multimodal insights that information brokers will faucet.
“Nemotron RAG is on top of what we call the MMTab, or the Massive Multilingual Text Embedding Benchmark, with strong multilingual performance while using less computing power memory, so they are a good fit for systems that must handle a lot of requests very quickly and with low delay,” Briski mentioned.
Nemotron Security detects delicate information so AI brokers don’t unintentionally unleash personally identifiable information.
