Developing on NVIDIA® Jetson™ for AI on the Edge

NVIDIA Jetson Nano Developer Kit

The NVIDIA Jetson Nano Developer Kit is a $99 Jetson built for Maker and AI projects. Looky here:


There have been several models of the Jetson over the last 5 years, starting with the Jetson TK1 and most recently the Jetson AGX Xavier. Each model is much more powerful than its predecessor in computing power, with increases in memory, number of CPU cores, storage and so on. And with each new model, the price increased.

Now we have an entry level version! The Jetson Nano uses a variant of the chip in the Jetson TX1.

Hardware and Stuffs

Earlier we covered the hardware specifications of the Nano. You can also get the details straight from the Tech Sheet at NVIDIA.

As is usual Jetson system architecture, the Jetson Nano Module connects to a carrier board which contains physical access to all of the different I/O connectors. The connector between the module and the carrier board is a little different than the other Jetsons, this one being a 260 pin SO-DIMM connector.

One of the nice features of the Jetson Nano Dev Kit is that there are 4 USB 3 connectors. These 4 USB connectors go internally through one USB hub to the Nano.


There are two ways to power the developer kit. The first is to provide 2A @ 5V to the micro-USB connector. Many common phone chargers can supply this amount of power. For more power hungry applications, you can provide 4A @ 5V to the barrel jack after putting a jumper on the power selection pins. The jumper determines which power jack to use.

The extra juice can add power to the USB ports. Think of the USB ports as two stacks of two, with each stack able to provide 1A. The GPIO pins can supply up to 2A. You can mix and match to meet your application requirements, but remember that you only have 4A available.

Note that at full throttle, the Jetson Nano by itself can use more than 2A. You can use the supplied nvpmodel utility to set the power envelope to use 5W, or 10W.


Speaking of GPIO, there is a new software library to bit-bang the GPIO pins. The default device tree for the GPIO pins now mimics the Raspberry Pi, which means that many Raspberry Pi projects can work with little to no modifications.

In addition, Adafruit has ported their Blinka library to the Jetson, which allows access to the entire Adafruit project ecosystem. Good stuff!


Installation is straightforward. The Jetson Nano uses a Micro-SD card to hold the operating system. NVIDIA supplies a ISO image of the file system to flash the card.

You will need at least a 16GB MicroSD card. In the video, we use a Samsung 64GB MicroSD card. You know we love our GBs! I also grabbed a 5V Power Supply off of Amazon and jumpered the power input selector.

It is straightforward to flash the SD card using the instructions on the NVIDIA website: Getting Started With Jetson Nano Developer Kit. In the video, we flash from a Windows machine, but you can use a Macintosh or a Linux machine instead.

For you diehards out there, you can also command line it, but you probably don’t need help with that. NVIDIA helpfully provides the secret commands in their Linux documentation section on the above web page.


One of the nice things about using a disk image on the Nano is that all of the Jetson libraries are already installed. The Nano runs an Ubuntu 18.04 variant named L4T. The CUDA libraries are already installed, along with OpenCV with GStreamer support, cuDNN, TensorRT, VisionWorks and other libraries.

There are additional packages available for later installation, most notably deep learning support. This includes TensorFlow, PyTorch, Caffe, Keras and MXNet. ROS is also available.


Some folks like benchmarks. Here’s a great article benchmarking the Nano against the usual suspects, like Raspberry Pi 3 Model B+, ODROID-XU4, ASUS TinkerBoard and the rest of the Jetson family. The article is here: NVIDIA Jetson Nano: A Feature-Packed Arm Developer Kit For $99 USD.

If you’re into Deep Learning and more Nitty Grittys, here’s a great article from Dustin Franklin at NVIDIA: Jetson Nano Brings AI Computing to Everyone.


Setting up the Jetson Nano Developer Kit is now straightforward, and can now be done from your platform of choice. We’ll soon start looking at how to use this little pup in some of our projects. Stay tuned!


You will see many references to ‘Tegra’ in the Jetson world, this is in reference to the chip family. The Jetson is based on a a Tegra chip.


20 Responses

  1. Do you think there would be any issues for the Nano using an always on 40mm Fan for cooling, instead of a PWM one?

    1. That should work, though I haven’t tried it. You have the extra power drain, of course, but I wouldn’t think it would hurt anything. Thanks for reading!

  2. Hello kanglow,
    I followed your blog to work on with jetson nano but facing problem.

    I have flashed the .img file to micro sd card, connected all the cables as suggested
    After I placed my sd card into the slot and power on the nano, it is not reading the OS.
    what’s going wrong, please help out, i am stuck very badly.

  3. All the specs I have found so far regarding the nano are pretty oversimplified. How can I find out things like number and specs of the tensor cores, GPU architecture family, exact mounting hole spacing for the board, etc? Thanks!

  4. Hi, I have setup Jetson nano following the steps in Getting Started With Jetson Nano Developer Kit link. I want to work with Jupyter notebook on Jetson, Can you please tell me if Jupyter notebook is pre installed with the package( Jetson Nano Developer Kit SD Card Image) or should it be installed separately. If already installed can you let me how to access it or if I should install separately can you please provide me the link?

  5. Hi,
    I am running a PyTorch model in jupyter notebook on Jetson nano and it has been very slow. The actual training doesn’t even start and everything just freezes when I run the training cell. Can you please help me or suggest a link where I can look up for this?

  6. I am actually working on a DNN project that I have to implement on jetson nano for real time applications but I met a few problems and I am in dire need of help considering that I am new to linux and I have very limited time left for completion. (have to get done by 8th of june,2021)

    so I am done with the initial booting up of the jetson nano (flashing with SDcard image and starting it up, I have a wifi module connected too and set up the swap.)
    from here on, I’m seriously confused about what to so because I am not sure what results shall I get If I check my docker container information . It gives me 0 for containers, images and all. I don’t know whether to follow the tutorials by word or to download from dockerhub just like that. Haven’t even been able to access my jupyter notebook yet. I’d really appreciate a detailed and well explained stream of steps that need to be followed up till the point where i can simply run my own code.

    kindly help me urgent basis if possible

    1. There are many resources on the web that can help. You can take the ‘Getting started with AI on Jetson Nano’ from the NVIDIA Deep Learning Institue:

      There are several getting started with AI videos from NVIDIA:–uQRRDTPsJDp4o0xbzkoyf8
      These cover the basics of set up and getting started, inferencing and training.

      Good luck on your project!

      If you have specific questions, please ask them on the official NVIDIA Jetson Nano forum, where a large group of developers and NVIDIA engineers share their experience:

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