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

2026-03-10 · Source: NVIDIA Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

NVIDIA is advancing edge AI for physical systems, moving large language models (LLMs) and vision language models (VLMs) from cloud data centers to on-device deployment using its Jetson platform. This shift addresses latency, power, and cost constraints inherent in physical AI applications. The Cat AI Assistant, running on NVIDIA Jetson Thor with Nemotron speech models and Qwen3 4B, demonstrates real-time, local natural voice interaction for mini-excavator operators. Other examples include Franka Robotics' FR3 Duo using NVIDIA GR00T N1.6 for onboard perception-to-motion control, and the NYU Center for Robotics' YOR robot performing intricate pick-and-place tasks. The Jetson platform supports a wide range of open models like Gemma 3, gpt-oss-20B, Mistral 3, NVIDIA Cosmos, and PI 0.5, enabling developers to build private, always-on AI assistants and generalist robot skills with full data privacy and zero API costs.

Key takeaway

For AI Architects and Robotics Engineers designing autonomous physical systems, consider NVIDIA Jetson for deploying generative AI models at the edge. This approach significantly reduces latency and ongoing cloud compute costs, while enhancing data privacy and system reliability. Your teams should explore Jetson's support for open models like GR00T and Cosmos to build real-time, intelligent agents and robots, accelerating development cycles and enabling robust, on-device decision-making.

Key insights

Edge AI platforms like NVIDIA Jetson enable real-time, private, and cost-efficient deployment of large AI models on physical systems.

Principles

• On-device AI reduces latency and operational costs.
• Integrated compute/memory simplifies hardware design.
• Open models foster developer innovation.

Method

Deploy generative AI models locally on edge platforms like NVIDIA Jetson, utilizing optimized runtimes such as vLLM for low-latency inference and full data privacy without cloud dependency.

In practice

• Use Jetson for private AI assistants.
• Deploy VLMs for robot perception and action.
• Fine-tune open models for specialized agents.

Topics

• Edge AI
• Physical AI
• NVIDIA Jetson
• Generative AI Models
• Robotics

Best for: AI Architect, NLP Engineer, Computer Vision Engineer, AI Engineer, Robotics Engineer, AI Researcher

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Blog.