As Open Models Spark AI Boom, NVIDIA Jetson Brings It to Life at the Edge

The Cat 306 CR mini-excavator weighs just under eight tons and fits inside a standard shipping container. It's the machine a contractor rents when the job site is tight: a utility trench near a foundation, a basement dig in a dense neighborhood.

The cab is roughly the size of a phone booth. The operator sits close to the controls, two joysticks, multiple functions per hand. It takes time to learn. It takes longer to speed up.

At CES earlier this year, that machine answered questions.

In the demo, the Cat AI Assistant ran on NVIDIA Jetson Thor, an edge AI platform built for real time inference in industrial and robotic systems, NVIDIA Nemotron speech models are used for fast and accurate natural voice interactions. Qwen3 4B, served locally via vLLM, interprets requests and generates responses with low latency, no cloud link required.

Beyond enterprise innovation, open models unlock new possibilities for developers to build and experiment freely. Running OpenClaw on NVIDIA Jetson enables developers to create private, always-on AI assistants at the edge - with zero application programming interface cost and full data privacy.

All Jetson developer kits support OpenClaw, offering the flexibility to switch across open models from 2 billion parameters to 30 billion. With a frontier-class AI assistant running locally, users can power morning briefings, automate daily tasks, perform code reviews and control smart home systems - all in real time.

From the Cloud to the Edge For most of their recent history, open models lived where it was easiest to support them.

They ran in data centers, backed by elastic compute and persistent networks. Cloud deployments carry costs in latency and ongoing compute spend that scale with every query.

Physical systems optimize for something else. Low latency because machines interact with people and environments. Limited power because devices have hard limits. And consistent behavior because variability introduces risk.

There's also a supply question. Memory shortages have driven up costs across the industry. Jetson brings compute and memory together in a system-on-module, accelerating customer hardware design and making sourcing and validation easier than with discrete component approaches.

And as models have grown more efficient, developers have also started asking a different question. Not which model performs best in isolation, but where it makes sense to run.

More often, the answer is on the device, starting from Jetson Orin Nano 8GB for entry-level generative AI models.

Building Autonomous Physical AI Systems at Scale For physical AI systems, generative AI models are expanding what's possible.

Caterpillar's in-cab Cat AI Assistant, which is in development, runs speech and language models locally alongside trusted machine context, supporting operator guidance and safety features.

At CES, Franka Robotics showed what that looks like in robotics. The company's FR3 Duo dual-arm system ran the NVIDIA GR00T N1.6 model end-to-end onboard, perception to motion, no task scripting. The policy executes locally.

In robotics research, the SONIC project from NVIDIA's GEAR Lab trains a humanoid controller on over 100 million frames of motion-capture data, then deploys the resulting policy on a physical robot where the kinematic planner runs on Jetson Orin at around 12 milliseconds per pass. The policy loop runs at 50 Hz. Everything executes onboard.

The pattern reaches into the developer community. A team from UIUC's SIGRobotics club built a dual-arm matcha-making robot on Jetson Thor running the GR00T N1.5 model. It took first place at an NVIDIA embodied AI hackathon.

This research momentum continues at the NYU Center for Robotics and Embodied Intelligence. The group recently ran its YOR robot on Jetson Thor, using NVIDIA Blackwell compute to handle the heavy processing required for AI-driven movement. Early results show YOR performing intricate pick-and-place tasks with better generalization to new objects and robustness to scene variation, accelerating readiness for a wide range of household tasks like cooking and laundry.

Independent researchers are finding the same. Andr s Marafioti, a multimodal research lead at Hugging Face, built an agentic AI system on Jetson AGX Orin that routes tasks across models and schedules its own work. Late one night, the agent sent him a message: Go to sleep. Everything will be ready by morning.

Developer Ajeet Singh Raina from the Collabnix community has shown how to run OpenClaw on NVIDIA Jetson Thor for a personal AI assistant that runs 24/7. This setup allows for private large language model inference for the user's own data while the system manages emails and calendars through a local gateway.

Jetson Is the New Standard NVIDIA Jetson has become a common platform for running open models at the edge.

It supports a wide range of open models and AI frameworks, giving developers flexibility for almost any generative AI workload at the edge.

Model benchmarks are available at Jetson AI Lab, along with tutorials from the open model community. Jetson Thor delivers leadership inference performance across all major generative AI models.

Gemma: Built on Google's Gemini research, Gemma 3 is a versatile workhorse for Jetson. It is multimodal out of the box, which means it can see and talk in over 140 languages. On Jetson Thor, it handles a massive 128K context window. This makes it perfect for robots that need to remember a long list of complex or multistep instructions.

gpt-oss-20B: This model from OpenAI lowers the barrier to deploying advanced AI by delivering near state-of-the-art reasoning performance in a model that can run locally on Jetson Thor and Orin for cost-efficient inference.

Mistral AI: The new Mistral 3 open model family delivers industry-leading accuracy, efficiency an

LINK:	https://blogs.nvidia.com/blog/jetson-generative-ai-edge-oss/...
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