Jetson Orin Nano Super Developer Kit

The NVIDIA Jetson Orin Nano Super Developer Kit exceeded every expectation. For its size, this thing delivers incredible performance — fast boot times, smooth CUDA acceleration, and outstanding handling of AI workloads. Running local LLMs, vision models, robotics stacks, and edge-compute pipelines feels effortless. The build quality is solid, setup is straightforward, and the system stays stable even under heavy loads. I’ve tested everything from PyTorch models to engineering diagnostics and it never struggles. For anyone working on edge AI, embedded systems, or real-time machine learning, this is an absolute powerhouse. Highly recommended if you want serious AI performance in a compact, efficient developer kit. This is hands-down one of the best edge-AI boards available right now.

Slight pain on getting newer jetpack installed for non-Linux person, but i got it. I had Ollama Phi3 up and running very quickly I'm impressed and looking forward to playing with it more.

First things first, this board is absolutely beautifully designed. The location of the SD Card and where you can add your NVMe drives make logical sense. It ships with factory firmware that requires an update before use. It is a bit of work to find the firmware update and is a rather large file that you will then need to flash onto an SD Card using BalenaEtcher, which is about 30 minutes of waiting depending on your download and cpu speeds. The UEFI bios is very well organized and structured and does have TPM 2.0. It does not have an OS installed by default, so you will need to install one via SD Card or NVMe slots. Which means you can use official Nvidia images or you can use custom ones. The official image is also a bit of a pain to find, but again, once you download it, you need to flash it onto an SD Card using BalenaEtcher. Your mileage may vary for how long this process will take. For me, it was around 10 minutes. The construction of this thing is super solid. Has a very solid base that the SBC connects to, the CPU is more of a Compute module setup so you could possibly change it for a newer MU unit later without needing a new base. The standard use case for a board like this is local LLM inference, my use case is currently getting my custom OS to boot on it and then move to local LLM inference later.

what a waste of time, not worth my sanity. another day and I'd likely take a sledge hammer to it. nvidia software, their os, the sdk, the code examples (jetson lab), all of it is just absolute garbage. first, you must have real computer (vm won't do) with intel and ubuntu 22.04 just to flash the nvme. then you find out nothing works. first clue was their "readme" link they placed on the desktop "for my convience", which doesn't work, points to nothing. snap needs downgrading before you can run any program. then there are the ai software examples from their own lab. I wasted a week so far trying. only ollama native or container work. I can't make anything else work, and these are their own "tutorials" for this board. all I learned from those is to stay far away from nvidia. I don't believe any of that software, the os and the tutorials are tested or that they are maintaned. their support forums have nothing useful. none of the speech or image or vision tutorials work, all I get is errors, or no response. docker containers start, but nothing listens on the ports I'm supposed to browse to. a swap file is necessary to run anything because the os and nvidia crapware already use about 2-3 GB, leaving very little for models. performance is disapointing, the advertised 67 tops is a lie, marketing bs. in the "super" mode it throttles down immediatelly, actually it trottles down in all power modes. the fan does nothing because it defaults to quiet mode, and you must find a way to set it to allow it to do its job of actually cooling the chip. every step is a struggle, hours of trying, hundreds of gigabytes of wasted downloads. I bought this nvidia dev kit because of the hardware specs:, 1024 cudas, 32 tensors, 2 pcie slots, gpio, 2 csi cameras. but it's all useless without working software and drivers and documentation, and nvidia people have no clue how to code. I know nvidia since mid 90s, their video card drivers were always horrible.

It takes some effort to set up, and the placement of the socket where the memory card is inserted isn't great. Once it is set up it works quite well.

This device is pretty sweet and for the price it was at was well worth it. It is a speedy little device and I am still learning things it can do. So far Emulation station it does phenomenal with, I am working on some AI generation and LLM stuff. Also testing this out with a digital camera for taking cleaner pictures and using profiles others have setup to get better night shots. There are a lot more things to do with this and it is a very versatile deal.

Works as expected and very useful in robotics

I LOVE THIS THING!!!! i got the 2 TB of ssd space after using it for a little while. The 8gb of RAM Is okay, could use more, but it gets the job done. I have almost every single LLM thats under 4B parameters installed on it and its running like a champion. I bought the Cube Nano case to go with it so its inside a little desktop style build. I also got the OLED display going which was a bit of a pain but looks so cool! I installed claude code on the jetson Orin Nano and had it figure out the OLED.