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 and customization capabilities for developers and enterprises. This family includes small, dense models ranging from 3B to 14B, fast and remarkably smart for their size. Jetson developers can use the vLLM container on NVIDIA Jetson Thor to achieve 52 tokens per second for single concurrency, with scaling up to 273 tokens per second with concurrency of eight.
NVIDIA Cosmos : This leading, open, reasoning vision language model enables robots and AI agents to see, understand and act in the physical world like humans. Both the 8B and 2B models run on Jetson to deliver advanced spatial-temporal perception and reasoning capabilities.
NVIDIA Isaac GR00T N1.6 is an open vision language action model (VLA) for generalist robot skills. Developers can use it to build robots that perceive their environment, reason about instructions and act across a wide range of tasks, environments and embodiments. On Jetson Thor, the full GR00T N1.6 pipeline executes onboard, delivering real-time perception, spatial awareness and responsive action.
NVIDIA Nemotron : A family of open models, datasets and technologies that empower users to build efficient, accurate and specialized agentic AI systems. It’s designed for advanced reasoning, coding, visual understanding, agentic tasks, safety, speech and information. The Nemotron 3 Nano 9B model effectively runs on Jetson Orin Nano Super with llama.cpp with 9 tokens per second performance.
PI 0.5: A VLA model from Physical Intelligence that enables robots to understand instructions and autonomously execute complex real-world tasks with strong generalization and real-time adaptability, while NVIDIA Jetson Thor delivers 120 action tokens per second to power responsive, low-latency physical AI deployment. Qwen 3.5: This family of models from Alibaba, including the latest Qwen 3.5 releases, offers a mix of dense and mixture‑of‑experts models that deliver strong reasoning, coding multimodal understanding and long‑context performance. Jetson Thor delivers optimized performance across Qwen models like the Qwen 3.5-35B-A3B model, which reasons at 35 tokens per second, making real-time interactivity possible.
Any developer can fine-tune these models to create specialized physical AI agents and seamlessly deploy them into physical AI systems. The NVIDIA Jetson platform supports popular AI frameworks from NVIDIA TRT, Llama.cpp, Ollama, vLLM, SGLang and more.
Developers can dive into Hugging Face tutorials — including Deploying Open Source Vision Language Models on Jetson — and catch the latest livestream . Learn from this tutorial and run OpenClaw on NVIDIA Jetson.
Join GTC 2026 next month to see it all in action. NVIDIA will show how open models are moving from data centers into machines operating in the physical world, including in a panel on the Future of Industrial Autonomy .
Watch the GTC keynote from NVIDIA founder and CEO Jensen Huang and explore physical AI , robotics and vision AI sessions.
NVIDIA Jetson Thor: Edge AI platform for real-time inference in industrial and robotics systems
NVIDIA Riva: Speech AI framework using Parakeet ASR and Magpie TTS
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://blogs.nvidia.com/blog/jetson-generative-ai-edge-oss/#primary
- https://blogs.nvidia.com/blog/author/chesu/
- https://blogs.nvidia.com/blog/jetson-generative-ai-edge-oss/#disqus_thread
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Informational only. No financial advice. Do your own research.