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Nvidia Cosmos Reason 2: Leading the Global Transition to Physical AI

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Nvidia unveils Cosmos Reason 2 and new Nemotron models at CES 2026, marking a pivotal shift toward Physical AI and generalist robots with 10x faster speech processing.

Can robots truly reason? Nvidia thinks so, and they’ve just released the brain to prove it. At CES 2026, the tech giant signaled a massive shift towards 'Physical AI,' where AI models move beyond chat interfaces and directly drive intelligent actions in the real world. CEO Jensen Huang's vision of embodied intelligence is no longer a future concept—it's arriving in the form of a sophisticated software-hardware ecosystem.

Nvidia Cosmos Reason 2 Physical AI: The Rise of Generalist Robots

The centerpiece of the announcement is Cosmos Reason 2, the latest vision-language model (VLM) designed for embodied reasoning. Building on the success of Cosmos Reason 1, which currently leads physical reasoning leaderboards, the new version allows physical agents to plan their next moves with the same fluidity that digital agents navigate workflows. This flexibility is crucial for enterprises looking to deploy robots in unpredictable human environments.

"Robotics is at an inflection point," stated Kari Briski, Nvidia vice president for generative AI software. She noted that the industry is moving from specialist robots limited to single tasks to "generalist specialist systems." These new robots combine broad foundational knowledge with deep proficiency in complex, task-specific skills, making them far more versatile than their predecessors.

Expanding the Nemotron Family for Agentic AI

Beyond physical movement, Nvidia is accelerating how AI agents communicate and stay safe. The new Nemotron Speech model is reportedly 10 times faster than existing alternatives, enabling real-time low-latency speech recognition for live applications. Alongside this, Nemotron RAG enhances multimodal data insights, while Nemotron Safety ensures that sensitive personally identifiable data isn't accidentally leaked during autonomous operations.

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