The NVIDIA Jetson Nano Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing. The developer kit can be powered by micro-USB and comes with extensive I/Os, ranging from GPIO to CSI. This makes it simple for developers to connect a diverse set of new sensors to enable a variety of AI applications. And it is incredibly power-efficient, consuming as little as 5 watts. Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started. The same JetPack SDK is used across the entire NVIDIA Jetson family of products and is fully compatible with NVIDIA’s world-leading AI platform for training and deploying AI software. This proven software stack reduces complexity and overall effort for developers.
Description
Additional information
| Asin | B084DSDDLT |
|---|---|
| Dimensions | 2.72 x 1.77 x 1.77 inches |
| Weight | 8.5 ounces |
| Manufacturer | NVIDIA Corporation |
Reviews (10)
No products in the cart. 
Muhammad Maroof –
Amazing product especially for kids who love Robo Technology.
Zach pinkerton –
After repairs from shipping damage. Not the sellers fault. It works great. I’m using it as the brain for my r2d2 project and so far it’s doing everything I ask it to.
TX-One –
The following is information that normally would be cited to the software and hardware providers … that is Ubuntu and Nvidia. Cites are not allowed here. But, would expect that one could search on the quoted sentences. I am writing this because I wasted a lot of time without knowing the following. It may or may not apply to your application. It does to my project.
[] “Ubuntu 18.04 LTS will be supported for 5 years until April 2023. ” This is May 2023, so no support.
[] The JetPack is provided from a Nvidia site special Ubuntu 18.04 download. The JetPack installed in the special Nvidia Ubuntu 18.04 allows one to work with the AI hardware. From Nvidia developer forums: “JetPack 4.6 will be the last major release for Jetson Nano”. On a separate page for continuing products: “The current Jetpack for products with continuing support is NVIDIA JetPack 5.1.1.” JetPack 4.6 is already a release behind. This seems to mean that you cannot update the OS because NVidia is not supporting the update.
[] EOL risk: “The following Jetson Developer Kits have reached End of Life: ” “Jetson Nano 2GB Developer Kit” It may be inferred that this 4 GB kit may soon follow? The support pages says that is solely up to the company and it can happen at any time. My projected use is into 2025 … so I can not take that chance.
Frozen In Time:
Frozen at Ubuntu 18.04.xx released 2021-09-17 is the NVIDIA Jetson Nano Developer Kit (945-13450-0000-100) because it is tied to JetPack 4.6. Current product and continuing supported product is at NVIDIA JetPack 5.1.1. Last there appears to be an EOL risk. I need a platform that at least is current through 2024.
Addendum: The first Jetson Nano was DOA and would not boot. The second Nano booted right up and was observed to be a really nice platform. Sadly, the man made impediments noted above indicate that it is near end of life. Also, please note that the 2 amp power supply called for on the Nvidia site would not power the platform … a 4 amp replacement was required.
Devin Canterberry –
I expected this to be, basically, a Raspberry Pi with a GPU. A computer I could flash with any arm64/aarch64 OS and it Just Work. Boy, was I wrong.
Despite being called a “Dev Kit”, this is just the device itself. Nvidia just calls it a ” non-production-grade” product, so Dev Kit is just a euphemism for it being a low-quality product, on purpose.
The docs say you need a 5V 2A power supply, but that is not true. It will not even boot unless it has at least 4A, so make sure you buy the right one.
The Jetson Linux SD card image in the Getting Started docs is half a decade old. Even the most current version of Jetson Linux available for this product is based on Ubuntu 18.04, which reaches EOL in May 2023. Nvidia is *not* releasing updates for Jetson Linux that are compatible with this product, so it will be obsolete and wholly unsupported by both Nvidia and Canonical after May 2023. So you’re on your own.
Even after getting Jetson Linux up and running, it’s stuffed with bloatware like OS-integrated Facebook search (circa 2018) and a variety of games and a vastly outdated version of the whole Libreoffice suite. The default desktop theme and wallpaper is nausea-inducingly garish, reminiscent of the fad around lightning and dragon themed bowling shirts, circa 1998.
It is possible to upgrade the OS from Ubuntu 18.04 all the way up to 21.04 if you’re motivated enough. Each upgrade causes more volatility and instability, though, so YMMV and if you’re planning to do this, be very cautious and make backups along the way. I spent 7 hours attending this process (yes, you have to attend it because along the way, there are random confirmations you actually have to read, because sometimes the default is to abort the process, setting you back an hour or more. Ask me how I know this). After carefully upgrading, one major version at a time (bionic to focal, focal to jammy), the upgrade from jammy to koala bricked the OS and none of my recovery attempts were fruitful.
The problem with just using Jetson Linux right out of the box, without upgrading, is that you have to work within the time capsule of what was available at that time. Modern tools, AI models, etc, developed since will either not have the necessary dependencies, or taken as a whole, will not work with such an antiquated system. Combine that with this product being effectively abandoned with no future in sight for official support or continued development, and even if you’re a seasoned, highly competent software engineer, you’re in for a world of hurt.
If you do buy this product, I hope you have a lot of patience and tolerance for disorder and chaos. You’re going to need all of it.
genesis hafalla –
good
jack crossfire –
Getting a model to work on it requires porting it to tensorrt FP16, otherwise it’ll run extremely slowly & require a swap space. In tensorrt, there’s a 3x speed improvement over software int8. Very few models can successfully be ported to tensorrt & it’s very laborious to get the lucky few working. Nvidia stopped supporting tensorrt for the jetson nano years ago so there aren’t going to be any more models which run on it.
They claim to now be supporting the jetson orin & above, but for that price a cheaper option is going to be a gaming laptop or a single board x86 with an M2 to PCI adapter.
murat –
I couldn’t start both of the Nano jetson kits. I called NDIVIA support from India, a technician event listing.
Need to set the jumper to to work
Eric T. –
I hope Nvidia keeps producing these. The price/performance point is perfect for the application I am using it in.
Tyler –
Exactly as it describes.
Science4Life –
Jetson Nano is great for not only robotics/edge AI, you can use ML for science usage such as medical imaging or environmental data to speed up your workflow. From personal experience even an underclocked dual-core power save mode on the Jetson Nano will still be faster on CUDA AI/ML tasks than a Raspberry Pi 4, however your workflow may vary. If you use AI/ML that isn’t optimized for CUDA, in some cases a Pi 4 raw CPU compute can edge out the Nano. I would say if you pair a Pi 4 with any AI/ML accelerator it’ll cost more than a Jetson Nano and your mileage is still going to vary.
Depending upon how you use a Jetson Nano, for robotics/automation you can actually run four cameras via USB and use the camera interface. Performance wise if you do opt to run a Jetson Nano using USB power, your mileage is going to vary as not all USB power adapters provide a stable voltage which means checking the specs–I reused a Canakit USB power adapter from a retired Pi 3 and never had any voltage warnings but if you plan to run a Jetson Nano hard like a Pi 4 you’ll want to use the barrel power adapter for extra power stability when using multiple USB devices+GPIO. Thermal wise I’ve compared a fanless vs fan equipped Jetson Nano, even under sustained load the heatsink size prevents it from thermal throttling too much. This B01 version has two camera connectors which is geared for stereo imaging however you can run two cameras at a small performance loss and also fixed the networking issue which occurred on the original Jetson Nano A01/A02.
From a performance per watt/dollar ratio, if you’re going to dive deeper into AI/ML a Jetson NX is more ideal. With a Jetson Nano if you’re pushing four cameras and LIDAR it’ll require a bit of tweaking to get optimal performance and still remain at about 3.5GB of memory usage.