Run #LLMs# on @NVIDIARobotics #Jetson# without x86 ONNX export. 🚀 #TensorRT-Model-Connect# on reComputer Classic J5012 (#AGXOrin# 64GB, #JP7#.2). @huggingface checkpoint → trtmc build → .bundle on device trtmc run for text gen Tested: #Qwen3-4B# FP16 (~41GB container peak, 64GB recommended) No ONNX hop. Edge LLM/agent ready. Would you use on-device TRTMC for prototyping, or keep ONNX export for production? 👇 Full guide: Get your own one: #NVIDIA# #Jetson# #TensorRT# #TRTMC# #AGXOrin# #EdgeAI# #LLM# #PhysicalAI# #SeeedStudio#

Today I unboxed a Jetson AGX Orin 64GB. This is the on robot box. Cameras, CAN, capture, Isaac ROS. The nervous system for whatever I bolt it to. The rest of the lab: 2x DGX Spark for GR00T train and serve (Isaac Sim runs here too) RTX 4080 / 64GB DDR5 for Sim, demos, and Cosmos Physical AI, day 1. @NVIDIARobotics

$NVDA UNVEILS JETSON ORIN NANO 2 NVIDIA introduced a new entry-level edge AI computer for robots, delivery and inspection drones, and vision systems. Jetson Orin Nano 2 delivers 78 TOPS of AI compute with 8GB of memory and an 8-core Arm CPU. It offers 2x the inference performance of Orin Nano Super in the same form factor, while using 40% less power to deliver the same performance in 15W mode. It can locally run optimized LLMs and vision-language models including Nemotron, Cosmos, Gemma 4 and Qwen 3. Cognex, Doosan Bobcat and Matic are among the first adopters, while Alphabet’s Wing plans to evaluate it for real-time perception and reasoning in delivery drones. The module and developer kit are expected in the first half of 2027.

Ukraine says it found an NVIDIA Jetson Orin NX module inside Russia’s S-71M “Monochrome” cruise missile. The Jetson Orin is a small computer originally designed for civilian uses The version found in the missile has 16GB of RAM, an 8-core Arm CPU and an NVIDIA Ampere GPU with 1,024 CUDA cores. According to Ukrainian intelligence, the hardware could be used to process images from an onboard camera and help the missile identify targets NVIDIA says its Jetson modules are consumer products and are not designed for military use or officially sold in Russia but can end up in secondary markets

⚡ Fine-tune and quantize AI models directly on NVIDIA Jetson. Our new Jetson AI Lab tutorial shows how to use Unsloth and memory-efficient QLoRA to customize models, export quantized GGUF files, and run them locally with llama.cpp. Follow hands-on examples for: 🔹 NVIDIA Nemotron 3.5 Lightning on Jetson AGX Thor 🔹 Qwen3.5-4B on Jetson Orin Nano Start optimizing:

$NVDA is bringing more AI inference directly onto robots and drones with Jetson Orin Nano 2 delivering 2x the performance at lower power. That means more language and vision models can run locally at the edge without relying on the cloud.