RAM | 4 GB |
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Chipset Brand | nvidia |
Wireless Type | Bluetooth |
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NVIDIA Jetson Nano Developer Kit
Brand | NVIDIA |
Memory Storage Capacity | 16 GB |
Connectivity Technology | USB, GPIO |
CPU Manufacturer | ARM |
Wireless Communication Standard | Bluetooth |
About this item
- NVIDIA Jetson Nano developer kit is a low-cost AI computer. It delivers the compute performance to run modern AI workloads at unprecedented size. It is incredibly power-efficient, consuming as little as 5 watts.
- It is supported by NVIDIA Jetpack, used across the entire NVIDIA Jetson family of products, reducing complexity and overall effort for developers, learners, and makers.
- NVIDIA Jetson Nano Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost. 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. It’s incredibly power-efficient, consuming as little as 5 watts. Jetson Nano is also supported by NVIDIA Jet Pack, available using an easy-to-flash SD card image, making it fast and easy to get started. This proven software stack reduces complexity and overall effort for developers.
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From the manufacturer
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NVIDIA Jetson Nano
Jetson Nano enables the development of millions of new small, low-power AI systems. It opens new worlds of embedded IoT applications, including entry-level Network Video Recorders (NVRs), home robots, and intelligent gateways with full analytics capabilities.
![developer kit, computer, nvidia jetson nano](https://m.media-amazon.com/images/S/aplus-media/vc/e84bfa60-f5ad-465b-8704-af5f570d6c8b.__CR0,0,600,600_PT0_SX300_V1___.jpg)
NVIDIA Jetson Nano Developer Kit
This developer kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.
![jetpack sdk, jetson nano](https://m.media-amazon.com/images/S/aplus-media/vc/34a3765b-ac33-4cb7-8a2d-5093d9c118d8.__CR0,0,600,600_PT0_SX300_V1___.jpg)
NVIDIA JetPack SDK
JetPack SDK is the most comprehensive solution for building AI applications and is fully compatible with NVIDIA’s AI platform for training and deploying AI software. Flash your Jetson Nano Developer Kit with the latest OS image, install developer tools for both host computer and developer kit, and jumpstart your development environment with libraries, APIs, samples and documentation.
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A New Dimension in AIJetson Nano is the smallest Jetson device, but it delivers big when it comes to deploying AI to devices at the edge. Use the developer kit to build your own project, or use it to prototype a full production-ready AI solution. |
Big Compute CapabilityJetson Nano delivers 472 GFLOPs for running modern AI algorithms fast. It runs multiple neural networks in parallel and processes several high-resolution sensors simultaneously, making it ideal for a wide range of applications. |
Power-smart PerformanceJetson Nano frees you to innovate at the edge. Experience powerful and efficient AI, computer vision, and high-performance computing at just 5 to 10 watts. |
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Price | — | $1,999.00$1,999.00 | $219.00$219.00 | $195.50$195.50 | $89.90$89.90 | $27.29$27.29 |
Delivery | — | Get it as soon as Monday, Jun 17 | Get it Jun 28 - Jul 11 | Get it as soon as Monday, Jun 17 | Get it as soon as Monday, Jun 17 | Get it as soon as Monday, Jun 17 |
Customer Ratings | ||||||
Sold By | — | Amazon.com | WayPonDEV | Makeronics Direct | Makeronics Direct | Ethink |
hardware interface | usb | — | — | bluetooth | bluetooth | — |
RAM size | — | 64 GB | 4 GB | 64 GB | 64 GB | — |
connectivity tech | USB, GPIO | — | Ethernet | Wi-Fi, USB, HDMI | Wi-Fi, HDMI | HDMI |
wireless standard | bluetooth | bluetooth | 802 11 B | bluetooth | bluetooth | bluetooth |
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Product information
Technical Details
Brand | NVIDIA |
---|---|
Item model number | 945-13450-0000-000 |
Item Weight | 8.8 ounces |
Product Dimensions | 3.9 x 3.1 x 1.1 inches |
Item Dimensions LxWxH | 3.9 x 3.1 x 1.1 inches |
Processor Brand | ARM |
Number of Processors | 8 |
Flash Memory Size | 32 |
Manufacturer | NVIDIA |
ASIN | B07PZHBDKT |
Date First Available | March 19, 2019 |
Additional Information
Customer Reviews |
4.5 out of 5 stars |
---|---|
Best Sellers Rank | #3,641 in Single Board Computers (Computers & Accessories) |
Warranty & Support
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Product Description
NVIDIA Jetson Nano developer kit is a low-cost AI computer. It delivers the compute performance to run modern AI workloads at unprecedented size. It is incredibly power-efficient, consuming as little as 5 watts.
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Customers say
Customers like the quality, ease of setup, size and compatibility of the single board computer. For example, they mention it's a high-quality learning tool, great for small scale DL/ML projects and that it works with Ubuntu. Customers also like the value, and power. That said, some complain about the performance and the software.
AI-generated from the text of customer reviews
Customers are satisfied with the quality of the single board computer. They mention that it is a high-quality learning tool, a great concept, and a wonderful device for learning, prototyping, and even development. They also say that it's a powerful tool for machine learning applications.
"Very very nice bare bones development board. I'm using it for telemetry processing and graphic visual rendering of such live data...." Read more
"...And once you've got a trained model running, it's fast, fun, and inspiring to work with...." Read more
"...Very capable board for developing your AI software. Not nearly as fast as dedicated AI hadware or even a good desktop at learning...." Read more
"I simply like this a lot. It provides a nice entry level to dev for embedded parallel processing boards, think a little bit ahead about how you..." Read more
Customers find the setup of the single board computer to be easy. They say the tutorials are good and can get you set up fast. They also appreciate the memory and processing power.
"...and running whilst being fairly stable, the Nano is probably the easiest setup I've experienced in a while...." Read more
"...Very easy and quick to get going. nVidia did us right with this one...." Read more
"...at this point because once you've set up your toolchain it's very straightforward." Read more
"I liked how easy it was to get up and running. I liked how silent the board operates before it needs the fan, which it hardly does ever...." Read more
Customers are satisfied with the size of the single board computer. They mention that it is small but powerful, great for small scale DL/ML projects, and sufficient use of resources. The storage is cheap and small these days, and the board has plenty of memory and processing power. Some say that the Jetson Nano is quite capable, but is not meant to be a set top box.
"...as its primary purpose.2. - The Jetson Nano, while quite capable, is not meant to be a set top box by any means - if that is what you are..." Read more
"...Pros:- much more powerful than other boards- very compact- options for power supplies-..." Read more
"...Storage is cheap and small these days. Looking forward to building all the AI and image processing packages...." Read more
"Great tutorials.Plenty of memory and processing power.General purpose GPU programmatically compatible with top-of-the-line GPUs...." Read more
Customers are happy with the compatibility of the single board computer. They mention that it works with Ubuntu, has no incompatibilities, and is compatible with the second version of the Raspberry Pi camera. The board is also programmatically compatible with top-of-the-line GPUs, and runs Ubuntu Linux. Some AI tools work, but a lot of Linux software doesn't.
"...It is compatible with the second version of the Raspberry Pi camera, but I recommend getting a better one anyway, but it will work with that one, if..." Read more
"...very compact- options for power supplies- works with Ubuntu, so it is easier to get around and pretty.-..." Read more
"...It runs Ubuntu Linux, so you gotta love that!" Read more
"...Plenty of memory and processing power.General purpose GPU programmatically compatible with top-of-the-line GPUs...." Read more
Customers appreciate the value of the single board computer. They say it's an absolute bargain for the price and provides fantastic performance for the cost.
"...The RPi3B+ with its community and price point is also a good deal, but it is an entirely different type of learning platform...." Read more
"...Still a great device at a great price." Read more
"Nvidia provides an impressive dev board for delving into AI at an affordable price...." Read more
"Fantastic performance for the price. Add on a wifi expansion card and you're good to go for pretty much anything." Read more
Customers are satisfied with the power of the single board computer. For example, they mention it's plenty powerful and provides just the right amount of power and resources for personal use.
"...There are bugs in drivers.Pros:- much more powerful than other boards- very compact-..." Read more
"The Jetson Nano is my go to desktop it's plenty powerful for me and plays YouTube videos great...." Read more
"...of resources, instead of deploying a big rig, this provides just the right amount of power and resources for personal projects." Read more
"Very nice and powerful, but documentation and support simply isn’t there yet...." Read more
Customers are dissatisfied with the performance of the single board computer. They mention that the chip overheats and fails. They also say that the board is underpowered and has numerous compatibility and operation issues. Some customers say that it does not last a day and that the SDK only works on Ubuntu 16 and 18.
"...If you do and add peripherals, the board will crash rather easily...." Read more
"...The Pi's have excellent support, but are vastly underpowered...." Read more
"...constant crashes on Ubuntu from jetpack v4.2.0-4.2.2A really great idea that was executed poorly...." Read more
"...This should be a new field of RC car project. Hope it works well." Read more
Customers are dissatisfied with the software of the single board computer. They mention that the support from Nvidia is disappointing, they can't use some basic software like VLC on this machine, and that the board has serious software issues. Some customers also mention that they cannot write code on it and that they need another Linux.
"...However, you can't use some basic software like VLC on this machine which was a huge bummer for me...." Read more
"You would think this would be great board, by it had serious software issues. Cons:- The SDK only works on Ubuntu 16 & 18, not 19.-..." Read more
"...Only then did I figure out I cannot write code on it and I also need another Linux computer to program it. Maybe someday I can resume my AI desires..." Read more
"Disappointing Software support form Nvidia..." Read more
Reviews with images
![First impressions w the Nano are quite positive - Nvidia has delivered a high-quality learning tool](https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel._V192234675_.gif)
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Top reviews
Top reviews from the United States
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1. - The Jetson Nano, despite it's likeness to other Single Board Computers, it is categorically different than other SBCs with an ARM SoC. Indeed, the Jetson Nano is a System on Module, and is specifically built with Intelligent Systems design, Machine Learning, Robotics, etc., as its primary purpose.
2. - The Jetson Nano, while quite capable, is not meant to be a set top box by any means - if that is what you are looking for, the Nvidia Shield TV is a rather well developed platform and would be significantly more satisfying for the home theatre setting and at a fairly similar price (the Shield comes w a Power Supply, Internal Flash Storage, WiFi and Bluetooth, a custom build of Android TV, etc.).
I pre-ordered the Jetson Nano a few days after Nvidia announced its imminent release - after approx. 3wks or so, I finally received it. I had downloaded the Jetpack image file and flashed it to an SD card in anticipation of its arrival - so, setup was fast and simple. The Nano currently has Ubuntu as the primary OS, & while I am not a fan of Ubuntu, it is the cleanest OS I've encountered on an SBC, next to Raspbian and the Raspberry Pi. Compared to the Rock64, the Tritium H5, the Odroid XU4 etc., getting the Nano up and running whilst being fairly stable, the Nano is probably the easiest setup I've experienced in a while. Conversely, given the board's purpose, a ML learning platform, it has been a challenge for me for different reasons - but nothing I didn't expect.
As for the board, it does not come with a power supply and it can accept power via micro-USB, through the carrier board pinouts, or through a barrel jack. It is meant to run at 10W in default mode, but is capable of a 5W mode. To operate the board at 10W, do not power the board via micro-USB. If you do and add peripherals, the board will crash rather easily. I used the same 5V/4A power supply I ordered for my Odroid XU4 and it works perfect (you will need a jumper - pictured - to select how you will power the board).
The Nano requires a mSD card like most SBCs - a UHS-I, U3, Class 10 card is needed to get up and going properly; however, with 4 USB3 ports, I transferred my install to a spare SSD and it easily outperforms the mSD card. Also required - a WiFi/Bluetooth dongle or a PCIe Key A/E card, which can be installed under the module. Without, you will be forced to use the onboard Gigabit Ethernet connection.
The pictures seem to make the board look somewhat large - and while it is bigger than the RPi standard, it is still fairly small. It's approx. the same length of an 2.5" SSD and slightly larger compared to the width of an SSD. The Module does have a large heat sink - again, it appears to be much larger than it actually is - the heatsink mounts a 40mm x 40mm fan for perspective.
So, I know Nvidia has lost popularity over the last few years due to their GPUs; however, I have to admit, the Jetson Nano is a really great deal. Even if used as a standard SBC - the Nano is a great deal (the Shield is even better a deal for that though imo) compared to many other boards that cost the same or more. The benefit with the Nano is the access to the Jetson Package and a platform to learn and test the Cuda software. It will even have demos to see the Jetson's capabilities that come with the Ubuntu install.
Again, these are just my first impressions of the board - compared to several other SBCs I own, I can already say the Nano handily bests all of them. The RPi3B+ with its community and price point is also a good deal, but it is an entirely different type of learning platform. I would say if the Nano has sparked your interest and you're not expecting an even better Shield TV...and if you are adept with Linux, then the Nano is a great deal. I would definitely recommend giving it a go.
![Customer image](https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel._V192234675_.gif)
Reviewed in the United States on May 1, 2019
1. - The Jetson Nano, despite it's likeness to other Single Board Computers, it is categorically different than other SBCs with an ARM SoC. Indeed, the Jetson Nano is a System on Module, and is specifically built with Intelligent Systems design, Machine Learning, Robotics, etc., as its primary purpose.
2. - The Jetson Nano, while quite capable, is not meant to be a set top box by any means - if that is what you are looking for, the Nvidia Shield TV is a rather well developed platform and would be significantly more satisfying for the home theatre setting and at a fairly similar price (the Shield comes w a Power Supply, Internal Flash Storage, WiFi and Bluetooth, a custom build of Android TV, etc.).
I pre-ordered the Jetson Nano a few days after Nvidia announced its imminent release - after approx. 3wks or so, I finally received it. I had downloaded the Jetpack image file and flashed it to an SD card in anticipation of its arrival - so, setup was fast and simple. The Nano currently has Ubuntu as the primary OS, & while I am not a fan of Ubuntu, it is the cleanest OS I've encountered on an SBC, next to Raspbian and the Raspberry Pi. Compared to the Rock64, the Tritium H5, the Odroid XU4 etc., getting the Nano up and running whilst being fairly stable, the Nano is probably the easiest setup I've experienced in a while. Conversely, given the board's purpose, a ML learning platform, it has been a challenge for me for different reasons - but nothing I didn't expect.
As for the board, it does not come with a power supply and it can accept power via micro-USB, through the carrier board pinouts, or through a barrel jack. It is meant to run at 10W in default mode, but is capable of a 5W mode. To operate the board at 10W, do not power the board via micro-USB. If you do and add peripherals, the board will crash rather easily. I used the same 5V/4A power supply I ordered for my Odroid XU4 and it works perfect (you will need a jumper - pictured - to select how you will power the board).
The Nano requires a mSD card like most SBCs - a UHS-I, U3, Class 10 card is needed to get up and going properly; however, with 4 USB3 ports, I transferred my install to a spare SSD and it easily outperforms the mSD card. Also required - a WiFi/Bluetooth dongle or a PCIe Key A/E card, which can be installed under the module. Without, you will be forced to use the onboard Gigabit Ethernet connection.
The pictures seem to make the board look somewhat large - and while it is bigger than the RPi standard, it is still fairly small. It's approx. the same length of an 2.5" SSD and slightly larger compared to the width of an SSD. The Module does have a large heat sink - again, it appears to be much larger than it actually is - the heatsink mounts a 40mm x 40mm fan for perspective.
So, I know Nvidia has lost popularity over the last few years due to their GPUs; however, I have to admit, the Jetson Nano is a really great deal. Even if used as a standard SBC - the Nano is a great deal (the Shield is even better a deal for that though imo) compared to many other boards that cost the same or more. The benefit with the Nano is the access to the Jetson Package and a platform to learn and test the Cuda software. It will even have demos to see the Jetson's capabilities that come with the Ubuntu install.
Again, these are just my first impressions of the board - compared to several other SBCs I own, I can already say the Nano handily bests all of them. The RPi3B+ with its community and price point is also a good deal, but it is an entirely different type of learning platform. I would say if the Nano has sparked your interest and you're not expecting an even better Shield TV...and if you are adept with Linux, then the Nano is a great deal. I would definitely recommend giving it a go.
![Customer image](https://images-na.ssl-images-amazon.com/images/I/61EyvmQH1CL._SY88.jpg)
![Customer image](https://images-na.ssl-images-amazon.com/images/I/61kSfu9+gtL._SY88.jpg)
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![Customer image](https://images-na.ssl-images-amazon.com/images/I/715J86dn6cL._SY88.jpg)
![Customer image](https://images-na.ssl-images-amazon.com/images/I/71cMNZgj1pL._SY88.jpg)
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For anyone that is curious I used a 32 GB microSD card and during install it auto-expanded the root partition to utilize the entire card. I also attached a WD 314 GB USB HDD, formatted ext4, for project files compiling and large media storage. And using the barrel power connector instead of microUSB (via power bypass jumper).
Now here comes the Jetson Nano from NVidia, the top GPU manufacturer. The Nano comes with a fully supported LTS Ubuntu distribution fully customized and supported for the Jetson Nano. There are no incompatibilities nor issues. It also comes with a full developers kit for Artificial Intelligence development. Why? Well this sucker has an NVidiia GPU onboard. It's a Tegra on steroids. The community already have projects for facial recognition, self-driving robots, etc. It's quite the little board.
It boots off the SD Card, but also has a mini-PCI-E slot under the module. The distribution of Ubuntu is "minimized" to have a small storage footprint, but you can "unminimize" it to a full blown Ubuntu desktop distribution to restore any Linux utilities you want. So, if you are wondering where all of the man pages went, well you have to unminimize first.
sudo /usr/local/sbin/unminimize
Voila, it's a normal Ubuntu distribution with all of the Jetson Nano extras.
The GPU is exploited in the GStreamer app for video, if you want to watch 4K video.
It is compatible with the second version of the Raspberry Pi camera, but I recommend getting a better one anyway, but it will work with that one, if that is what you have.
The expansion bus is fully Pi compatible, but cards plug to side, and not on top of the board (due to the large heat sink being in the way).
It runs fine off of the USB power port, but if you are going to be doing some heavy computing, then I recommend powering it from the power plug with a 5V @ 5 amps supply.
But for any newcomers to the NVIDIA AI ecosystem, there's a lot of proprietary lingo to learn and it can take some time to understand what NVIDIAs tools are actually meant to do (the NGC cloud for example).
With that said, it's more of a documentation issue at this point because once you've set up your toolchain it's very straightforward.
Very capable board for developing your AI software. Not nearly as fast as dedicated AI hadware or even a good desktop at learning. It's still more than adequate for a portable device you can experiment with in the field (after you let a strong machine do the learning.)
It is a big plus that nVidia adopted the RPi pinouts on it's 40 pin section, but watch out for the small but not insignificant differences or you could release the magic smoke.
Top reviews from other countries
![](https://images-eu.ssl-images-amazon.com/images/S/amazon-avatars-global/default._CR0,0,1024,1024_SX48_.png)
Ermöglicht einen einfachen Einstieg für einfache KI Anwendungen.
Für den Preis gute Leistung und geringer Energieverbrauch. Da lassen sich schon einiger Prototypen draus basteln.
Für manche Anwendungen ist der RAM recht klein, dann lieber für eine der größeren Jetsons entscheiden.
Es gibt viel Zubehör.
![](https://images-eu.ssl-images-amazon.com/images/S/amazon-avatars-global/default._CR0,0,1024,1024_SX48_.png)
![](https://images-fe.ssl-images-amazon.com/images/S/amazon-avatars-global/default._CR0,0,1024,1024_SX48_.png)
私が買ったのは旧リビジョンですが、最近新リビジョンが売られています。商品写真は新旧混在しているのでどちらが送られくるか確信もてません。情報が追加されるのを待った方がいいかも。
![](https://images-eu.ssl-images-amazon.com/images/S/amazon-avatars-global/27ae5727-7b48-4aec-bcef-6ae9d007ea79._CR0,0,497,497_SX48_.jpg)
![](https://images-eu.ssl-images-amazon.com/images/S/amazon-avatars-global/default._CR0,0,1024,1024_SX48_.png)
1. 128 Nvidia graphics cuda cores
2. Easy to download operating system and install from Nvidia website
3. Very fast even from a memory card
4. Lots of documentation
5. Machine learning examples
6. Gigabit ethernet is faster than gigabit ethernet of PC
7. Temperatures never cross 45 even under moderate load, thanks to bundled heatsink
8. Excellent full fledged User Interface
9. Kernel is like real-time and everything feels snappy
10. 64 bit arm, wow!!
11. Excellent power management, runs in low power mode if you supply less power and runs like pc if you supply additional power which is well documented
12. Man it supports 4k monitor, kudos nvidia
Dislikes
1. No case or proper stand, no screws or latch, I have to build my own case by drilling holes in a plastic hard disk case to convert into a stand, used old ssd screws to tighten the motherboard
2. You are stuck with Ubuntu provided by Nvidia,not as open as raspberry pi or intel nuc which allows freedom to install any operating system of your choice
3. No wifi or Bluetooth
4. No jumper provided which is required to use power other than usb
5. Installing tensorflow takes forever as it builds all the packages on jetson, ideally they should be downloadable binaries
6. Missing Hardware Acceleration Since its arm64 chrome or firefox doesn’t support hardware decoding of videos, you are stuck with the arm processors eventhough there are 128 graphics cores
8. Hardware acceleration for video decoding is a closed box, very little documentation to achieve hardware acceleration for videos using kodi/vlc/mplayer