The $99 Jetson Nano – First Peek

Today NVIDIA announced the Jetson Nano, a $99 USD “Jetson for Everyone”. This is a preliminary look, details subject to change as we learn more. Update video 3/25/2019:

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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.

That’s different starting today! The new Jetson Nano is designed specifically for the Maker and AI space, with an budget-friendly price of $99 USD.


Some preliminary specs for the Jetson Nano module to get started:

Pictures, Now Please!

We all want to see the pictures. Show us!


NVIDIA continuously invests in software for the Jetson platform. With the introduction of the Jetson Nano, we are now on JetPack 4.2, with CUDA 10.0 and the usual cast of special libraries. In addition, TensorRT Next is unveiling for the first time on the Jetson platform.

What does this mean? It means that all of the Jetson software that we’ve been writing should port easily. Because we know the architecture, what we’re getting, and the comfort that the software investment is ongoing, life be good!

There are also new useful tools like the Jetson GPIO Python library. Tools like these allows using common sensors and peripherals, including many from Adafruit and Raspberry Pi.

Many popular AI frameworks like TensorFlow, PyTorch, Caffe, and MXNet are supported.

Jetson Nano Developer Kit

The Jetson Nano Developer Kit includes a Jetson Nano, along with a carrier board. The carrier board provides the “real world” connectors for Input/Ouput (I/O).

Like other Jetsons in the family, software configures how much energy the Nano consumes by setting the speed of the CPU cores and GPU. There are two modes, 5W mode and 10W mode. Note that this is for the module, additional power may be needed to drive peripherals.

You can power the Dev Kit using either through the micro USB connector or a barrel jack (jumper selectable). The Dev Kit runs on 5 volts. There are two ports for connecting a display. The first port is a HDMI 2.0 port, the second is a DisplayPort. The Nano can support two simultaneous displays.

The Input/Output on the carrier board is a little different that other Jetsons, in a good way! There are 4 USB 3.0 Type A connectors which interface to the Nano Module through a built in USB hub. The 4 USB ports are arranged in two stacks of two, with each stack capable of providing 1A. A Gigabit Ethernet connector is also available.

To support wireless, the Jetson Nano has a M.2 Key E slot which allows for the addition of industry standard wireless interface cards.

There’s the familiar 40 pin GPIO connector. There’s even silk screened labels. The GPIO connector can provide 3A of power to the pins. NVIDIA has spent a lot of time porting a Jetson GPIO Python library, very similar to that of Raspberry Pi. The library allows access the pins on the connector through the defacto standard API of the Maker world. There are several other libraries available at launch to support popular maker hardware, such as the Adafruit Blinka library.

The Dev Kit weighs 140g, with dimensions of 98mm x 80m x 29mm. Dimensions are approximate, I just eyeballed them.

Oh, and the box that the Nano is shipped in can also act as a stand.

Camera, Yes Please

Over the years, one of the most popular questions has been, “How do I connect a Raspberry Pi camera to the Jetson?” The usual answer was that you have to do some Johnny Genius magic, know way more than anyone should ever have to know about life, and only then you would have a chance of connecting the camera and get it to work. With the Jetson Nano Developer Kit, you plug the RPI camera into the camera port and you are good to go! All of the drivers and software are in the stock image. Also, other camera manufacturers like Leopard Imaging will have ready made solutions available.


This is a very interesting new product. For the price, there is a lot of computing here. In turn, this means that people will be able to make more compelling projects. Because of the large investment in CUDA software NVIDIA has made over the years, it is now straightforward to implement deep learning projects on inexpensive hardware. We’re really looking forward to working with the new baby Jetson!

We will be doing the usual JetsonHacks articles on the Jetson Nano in the coming weeks. Oh, and YouTube videos too. Stay tuned!


Links to Jetson Nano Resources

Jetson Nano Homepage

Jetson Nano Technical Blog

Jetson Nano Orders

Jetson FAQ

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