NVIDIA Jetson Orin Nano Super Developer Kit

It’s a 8GB shared GPU/CPU memory that can run quantized LLMs. The fan is not noisy and it run on Ubuntu 22.04 (you cannot update to 24.04+). Installation is a pain, but this was not my first rodeo. I have the latest lt4 drivers and CUDA 13.1 installed after doing some complex upgrades. Nothing is easy there, but the prepared docker containers are life savers. You can find containers set by functionality like Voice, LLM, Ollama, etc.

This is five star blazing fast awesomeness for just $250. It’s like a raspberry pi on steroids. BUT there’s an issue straight out of the box for me where the browser, chromium wouldn’t work. This issue can be easily resolved by downloading a previous version of Snap, then holding it at that version. There’s an article on jetsonhacks titled “ Why Chromium Suddenly Broke on Jetson Orin (and How to Bring It Back)” that will give you the terminal commands. Happy hacking

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.

Computers aren't toasters. NVIDIA thinks they are selling build-your-own toasters with these Jetson devices. Except there are no slots for the bread, and if you try to put them in, you might have to recompile the Linux Kernel. Against better judgment. So I'm not sure what the appliance is or will be. Good luck to the brave who have to enforce a standard for a particular build of this thing. I'm sure that the "stability" of a control build of you're device/application will be crushed by the never-ending parade of CVEs that plague networked devices. At the moment, applications on this nano will likely break on the newest version of Docker that patches a pre-auth RCE. Wait.. don't want to use Docker? Good luck if you try to do something bare metal; you're relegating yourself to the point in history this device came from. Also, if you plan to effectively air-gap, good luck matching your dependencies with system interfaces from 5 years ago. Their tune might be changing on this with more recent Jesons, but I'm sure there will still be lags by arrogantly trying to freeze a moment in time. Just because we have version control doesn't mean you can stop time or truly turn it back; in fact, with Git, you can rewrite history if you're so adventurous. I'm not sure anybody appreciates a denial of reality. Please, NVIDIA, just switch to fix forward. The people who need control builds will figure it out. I'm 0 for 2 for networking cards on getting wireless working with the kernel/bootload combo that does super mode (the one that came with it probably works, on the kernal that ships with the device, I didn't test that). Also, hosed my application by neively sudo apt update && sudo apt upgrade. This is where the docker issue above presents itself, you'll have to pin everything to stay stable. Also note that this doesn't ship with "super mode" firmware-wise, which is an Odyssey adventure to enable. Just get the NVIDIA driver manager (sign the TOS), install the newest old one (5.w.e), then install the newest new one (6.w.e) for this device... no prob, your favorite AI tool will help you. People in this review column are saying to be cautious: this is a "dev device," which I guess means something somewhat accurate, but to be more specific, this is the type of thing you buy and feels like false-capability advertising because the juice isn't worth the squeeze unless you absolutely need CUDA at this price point. I think that's what people mean when they say it didn't "perform" as expected. If it does perform as expected, it'll probably take me more than a hot minute to figure that out if I didn't have something better to do. On a positive note, the form factor is much more compact than expected. Very cute.

so far, its been pretty good, BUT NVIDIA drivers and Linux..... DUDE... its a PITA. When you do get the system up and running with linux drivers with all of the CUDA and the python support is finally working right (NVIDIA keeps changing python libraries without keeping a steady version numbering on their wheels or just deleting them without letting people know) its good. Currently using mine to build Ai models using transformers on market history for daytrading crypto. Works pretty good, i have my script on github... thing is i keep finding little changes i can add to the scrypt so i've only posted the main scrypt on github, and haven't settled on a new updated version yet till i work the bugs out of the jetson and the scrypt.

I’ve been extremely impressed with the NVIDIA Jetson Nano Super Developer Kit 8GB. For anyone seriously interested in exploring local AI, edge inference, robotics, or embedded AI systems, this little board is an absolute hammer for the price and power envelope. Performance-wise, I was able to achieve over 20 tokens/sec running an 8B model locally, which honestly exceeded my expectations for hardware in this class. NVIDIA’s CUDA ecosystem, TensorRT support, and overall AI tooling make this platform feel much more capable than its size would suggest. It punches well above its weight. That said, I do want to give one honest caveat: setup can be challenging. I’ve configured two of these boards now, and both took nearly a full day of troubleshooting, flashing, configuring, and tuning before everything was stable and running correctly. This is definitely more of an engineer/developer platform than a consumer plug-and-play device. However, if you’re comfortable working through Linux setup, drivers, SDKs, containers, or AI frameworks, the payoff is absolutely worth it. Once configured properly, this thing becomes an incredibly capable local AI platform. Highly recommended for developers, makers, robotics enthusiasts, and anyone wanting to learn real edge AI without spending workstation-level money.

This thing is a colossal pain in the ass to set up. My advice, skip the messing around with SD cards etc. Take a old laptop, install Nvidia Ubuntu with console, and install it directly to the nvme (get one too, you'll thank me). No cards no bs. You will probably need to build llama.cpp from source to integrate the cuda cores etc so it can take care of the hardware for inference. But once that's all done and set up, it runs great. I keep it at max power, running qwen 3.5 3B with vision. And I get around 16+ tokens a second. Pain to set up but worth it.

Good Products, Value fo rMoney

アメリカからの発送でしたが、当初の予定より早く届きました。 物も正規品で、他の方もコメントされていたとおり日本向けコンセントが同梱されてます。wifiカードとアンテナケーブルは接続済みでした。 国内での購入より、日にちはかかりますが、関税手続きの費用込みでも安く手に入りました。 注意点は、モニター出力がHDMIではなく、ディスプレイポートです。下名はHDMIと思いこんでおり、変換アダプタを慌てて購入しました。

Excellent

Yes

Semplicemente perfetto sia il prodotto che il servizio di consegna.

This product would not fire up. I tried many ways to "fire up" the NVIDIA Jetson Orin Nano including making sure the power supply and source were correct but it made no difference. The Jetson Nano would power up for a few minutes then cut out.