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Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson

4 September 2026 at 16:21
Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...

Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run locally on edge hardware. Developers building agents have had to route inference through a data center, adding network dependency, increasing costs, and exposing data that may need to stay on device. That constraint is lifting.

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Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control

19 August 2026 at 16:00
A robot picking up a tool.Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for...A robot picking up a tool.

Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for learning physical interactions, but their size can make on-device deployment difficult. This changes with the new NVIDIA Cosmos 3 Edge. Cosmos 3 Edge is a 4B omni-model (with a 2B NVIDIA Nemotron-based reasoner) in the Cosmos 3 family.

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Beyond VLAs: How World Action Models Reshape Robot Manipulation

4 August 2026 at 16:00
A GIF of a robot following directions” with a description of the actual robot, task, objects, and motion shown.A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene...A GIF of a robot following directions” with a description of the actual robot, task, objects, and motion shown.

A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene often fails when object shapes, positions, or lighting change. Generalizing to these new conditions requires the policy to understand the tasks underlying physics, not just mimic the demonstrations. This ability comes from the backbone it’s…

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Inside NVIDIA Halos for Robotics: A Full-Stack Functional Safety System for Physical AI

Physical AIβ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesβ€”is arriving faster than most expected. Traditional...

Physical AIβ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesβ€”is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI…

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How to Build In-Vehicle AI Agents with NVIDIA: From Cloud to CarΒ 

5 May 2026 at 16:00
The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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