The $399 NVIDIA Jetson Xavier NX Developer Kit is the new Jetson powerhouse on the block. Looky here:
The Jetson Xavier NX dev kit brings Jetson Xavier performance to help solve AI and robotics where you need some serious machine learning horsepower.
The entry level Jetson Nano is a good way to start for a lot of people, introducing the fundamentals of machine learning and GPU computing power. Consider the Xavier NX as a professional level to that, where you need beast mode to get serious work done.
Like other Jetsons, the Xavier NX dev kit can be thought of in two parts. The first part, the Jetson NX Module contains the compute and memory components. The second part is the carrier board, which provides affordance for connecting peripherals and providing power input.
The Jetson Xavier NX carrier board is the same layout as the Jetson Nano, with a couple of nice changes. First, there is a wireless card in the M.2 Key E slot on the underside of the board, pre-installed with antennas captured by the plastic base. Second, there is a M.2 Key M slot, also on the other side of the carrier board, which affords the means to install expansion items such as a NVMe SSD.
Like the Jetson Nano, the Jetson Xavier NX runs from a micro SD card. Unlike the Nano, the Xavier NX only runs from the supplied 19V power supply through the barrel jack. While you can power the Nano via the micro USB 2.0 port, the Xavier needs more power than can be supplied over that port. The Xavier runs in either 10W or 15W power profiles, the carrier board supports up to 5A@19V.
Here’s a list of items we used in the video:
- NVIDIA Jetson Xavier NX Developer Kit
- Western Digital NVMe SSD
- A faster SSD: Samsung NVMe SSD
- Samsung EVO 64 micro SD card
- Dymo Label Maker
The Jetson Xavier NX includes special purpose machine learning hardware, including 48 Tensor Cores and 2 NVIDIA Deep Learning Accelerators Engines (NVDLA). Overall performance in machine learning tasks averages over 10x that of a Jetson TX2.
The folks over at NVIDIA wrote a great article “Bringing Cloud-Native Agility to Edge AI Devices with the NVIDIA Jetson Xavier NX Developer Kit” that goes over a lot of specs and performance information. Well worth the read.
Here’s a short version of the specs:
|GPU||NVIDIA Volta architecture with 384 NVIDIA CUDA® cores and 48 Tensor cores|
|CPU||6-core NVIDIA Carmel ARM®v8.2 64-bit CPU 6 MB L2 + 4 MB L3|
|DL Accelerator||2x NVDLA Engines|
|Vision Accelerator||7-Way VLIW Vision Processor|
|Memory||8 GB 128-bit LPDDR4x @ 51.2GB/s|
|Storage||microSD (not included)|
|Video Encode||2x 4K @ 30 | 6x 1080p @ 60 | 14x 1080p @ 30 (H.265/H.264)|
|Video Decode||2x 4K @ 60 | 4x 4K @ 30 | 12x 1080p @ 60 | 32x 1080p @ 30 (H.265) 2x 4K @ 30 | 6x 1080p @ 60 | 16x 1080p @ 30 (H.264)|
|Camera||2x MIPI CSI-2 DPHY lanes|
|Connectivity||Gigabit Ethernet, M.2 Key E (WiFi/BT included), M.2 Key M (NVMe)|
|Display||HDMI and display port|
|USB||4x USB 3.1, USB 2.0 Micro-B|
|Others||GPIO, I2C, I2S, SPI, UART|
|Mechanical||103 mm x 90.5 mm x 34.66 mm|
Some Pics natch – Click to Expand
The next big push in the Jetson ecosystem is Docker based containers. While Docker support has been on the Jetsons for a few releases, they are now going mainstream. NVIDIA has built a server ecosystem, NVIDIA NGC, which contains pre-trained AI models and other resources which serve as building blocks in AI application development.
A great example of this is shown in the video as a demo. The 4 applications that are running are containers, running 7 machine learning models in total.
This shows the power of the Jetson Xavier architecture, where you get desktop level performance in a power budget of only 15W.
NVIDA has made the demos publicly available. The scripts for the demo are on Github on the NVIDIA-AI-IOT account in the jetson-cloudnative-demo repository.
The NVIDIA Jetson Xavier NX Developer Kit is the real deal for edge applications and robotics. There’s enough horsepower to run several models at once, while at the same time maintaining a very small power budget.
To be clear, this is pro level. If you are just getting started, the Jetson Nano is a good starting place. On the other hand, if you have outgrown the Nano, have experience with machine learning and/or have demanding inferencing applications, certainly checkout the Jetson Xavier NX Developer Kit.