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NVIDIA Jetson Xavier NX Developer kit review – how to setup?

NVIDIA Jetson Xavier NX Developer kit review

Who need this NVIDIA Jetson Xavier NX Developer kit? For intelligent machine OEMs, start-ups and AI application developers who want to create breakthrough products, the Jetson Xavier NX Developer Kit delivers the capability to develop and test power-efficient, small form factor solutions with accurate, multi-modal AI inference. Get details in NVIDIA Jetson Xavier NX Developer kit review.

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Pre-trained AI models from NVIDIA NGC, together with the NVIDIA Transfer Learning Toolkit, provide a faster path to inference with optimized AI networks, while containerized deployment to Jetson devices allows flexible and seamless updates. Developers can now take advantage of cloud-native support to transform the experience of developing and deploying AI software to edge devices. Find details in NVIDIA Jetson Xavier NX Developer kit review.

Developer Kit Contents

  • NVIDIA Jetson Xavier NX module and reference carrier board
  • 19V power supply
  • 802.11 plug-in WLAN and Bluetooth® module with antennas
    (assembled underneath the carrier board)
  • Small paper card with quick start and support information

Pros & Cons – NVIDIA Jetson Xavier NX Developer kit


  • Moonlight works flawlessly (with the correct controller)
  • It has a very good network card
  • Unity browser based games run very smoothly once all the shaders are cached. (it is initially laggy but it smooths out over time)
  • Retro emulation, Jetson Xavier NX is a beast when comes to emulation and can easily run some of the more demanding GameCube and Wii titles. It runs about any handheld console that has a working emulator
  • Netflix, Disney+ and HBO! Yes! you have access to all of your favorite video streaming platforms since it is treated like a normal computer with a browser! this makes this hardware an excellent platform to be used with your non smart TV.
  • You have full access to a Linux terminal in a handheld form factor
  • YouTube runs well even in 4k making this a really good on the go solution for watching videos
  • Twitch streams work really nicely and you are not limited to the mobile app interface


  • This is a power hungry device. it is very hard to supply power if you want it to be portable form factor is really tall and custom carriers are outrageously expensive. the hardware is very heavy
  • L4T has a very small community and most software is not available to it
  • If you need a software you probably have to build it yourself
  • you will end up relying on a lot of Raspberry Pi tools and software and they are not optimized for this SOC
  • L4T has a lot of limitations and lack of documentation, proprietary drivers and software will lead to a lot of frustration

Specs – NVIDIA Jetson Xavier NX Developer kit

  • Size 4.1 x 3.6 x 1.2 inches (103 mm x 90.5 mm x 31 mm)
  • Display Output 1x HDMI / 1x DisplayPort
  • USB 4x USB 3.1 (Type-A), 1x microUSB 2.0
  • GPU Nvidia Volta with 384 CUDA cores, 48 Tensor Cores
  • Camera Ports 2x CSI-2 ports
  • GPIO 40-pin GPIO
  • TDP 15W, 10W (low power)
  • CPU 6-core Nvidia Carmel ARM v8 CPU with 6MB L2 cache and 4MB L3 cache.
  • RAM 8GB LPDDR4 (on-board)
  • Storage microSD / M.2 80mm slot
  • Connectivity 802.11ac Wi-Fi, Gigabite Ethernet


NVIDIA Jetson Xavier NX Developer kit is available now at $399 including the module, baseboard, Wi-Fi card, and power supply.

Good for

Jetson Xavier NX is perfect for high-performance AI systems like commercial robots, medical instruments, smart cameras, high-resolution sensors, automated optical inspection, smart factories, and other AIoT embedded systems.

NVIDIA Jetson Xavier NX Developer kit Review


At 4.1 x 3.6 x 1.2 inches, the Xavier NX Developer Kit is a little bit larger than two Raspberry Pi 3Bs next to each other.  A fan comes attached and, considering the 15-watt TDP of the chip, I don’t think passive cooling would be a good idea.


The SOM itself is relatively compact, bar a sizeable heatsink and fan assembly screwed to the top, and most connectivity — apart from a microSD slot for storage — is on the baseboard. This includes four USB 3.1 and one USB 2.0 Micro-B ports, a gigabit Ethernet port, one each of HDMI and DisplayPorts, two MIPI CSI-2 camera ports, and an M.2 Key E slot pre-populated with an AzureWave 2×2 802.11ac Wi-Fi and Bluetooth module.

Raspberry Pi compatible

The board has two interesting features that allow it to be compatible with some key Raspberry Pi accessories. Two CSI camera connectors will work with any Raspberry Pi camera module, including the new Raspberry Pi High Quality Camera. There’s also a 40-pin GPIO header which Nvidia says will work with Raspberry Pi HATs if you use the proper Python library when programming. 


Onboard of the SoM you get a hexa-core CPU using Nvidia’s custom Carmel ARM-based cores, a 384-core Volta-based GPU, and 8GB of LPDDR4x RAM @51.2 GB/s. The development board adds HDMI, DisplayPort, Gigabit Ethernet, 4x USB 3.1 ports, Wi-Fi, Bluetooth, 2x camera connectors, 40 GPIO pins, and an M.2 slot for an SSD!


While Graphic Processing Units are great for 3D gaming, it also turns out that they are good at running machine learning algorithms. Nvidia has a whole software eco-system based around its CUDA parallel computing and programming model. The CUDA toolkit gives you everything you need to develop GPU-accelerated applications and includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.

Cloud Native

The hardware’s only part of the story, though. NVIDIA’s pushing the Jetson Xavier NX as key to bringing what it calls “Cloud Native Computing.” Available across the Jetson range, specifications allowing, NVIDIA’s Cloud Native vision aims to turn machine learning developers on to containers — literally separating the applications from the operating system, allowing either to be updated at will and new applications to be quickly rolled out.

NVIDIA Jetson Xavier NX Developer kit Performance review

We ran some of Nvidia’s recommended benchmarks on the Xavier NX and compared them to results from the Jeston Nano (note that the Nano results come from Nvidia’s press materials on the Nano from March 2019 as we were not able to test the Nano. However, these numbers were published long beter the Xavier NX was announced).

A.I Test ModelJetson Xavier NX (fps)Jetson Nano (fps)
Inception v4317.111
Tiny Yolo568.925
Super Resolution157.215

With memory bandwidth measured at 32,351MB read and 32,103MB write in 1MB blocks — more than four times that of the Orange or Rock Pis — anything which does a lot of memory operations enjoys a major speed boost.

As you can see from the table above, on some of these models, the Xavier NX is as much as 20 times faster than the Nano. This added performance is what it allows it to not only react more quickly to incoming streams but to handle multiple applications at once, which is necessary when you want to create a robot that can move, talk and interpret human body language / speech at the same time.

The CPU isn’t the star of the show, though: It’s the GPU, and the NVDLAs, that are the reasons to buy a Jetson Xavier NX. NVIDIA claims these combined offer compute performance of 21 TOPS at INT8 precision, putting it below the 32 TOPS of the company’s range-topping and twice-the-price Jetson AGX Xavier Developer Kit but streets ahead of the previous-generation Jetson TX2 it’s designed to replace – a tenfold performance boost for the same power envelope in deep learning inference workloads, by NVIDIA’s reckoning.

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