Hands-On: NVIDIA Jetson Orin Nano Developer Kit
Hands-On: NVIDIA Jetson Orin Nano Developer Kit

NVIDIA’s Jetson line of single-board computers are doing something different in a vast sea of relatively similar Linux SBCs. Designed for edge computing applications, such as a robot that needs to perform high-speed computer vision while out in the field, they provide exceptional performance in a board that’s of comparable size and weight to other SBCs on the market. The only difference, as you might expect, is that they tend to cost a lot more: the current top of the line Jetson AGX Orin Developer Kit is $1999 USD

Luckily for hackers and makers like us, NVIDIA realized they needed an affordable gateway into their ecosystem, so they introduced the $99 Jetson Nano in 2019. The product proved so popular that just a year later the company refreshed it with a streamlined carrier board that dropped the cost of the kit down to an incredible $59. Looking to expand on that success even further, today NVIDIA announced a new upmarket entry into the Nano family that lies somewhere in the middle.

While the $499 price tag of the Jetson Orin Nano Developer Kit may be a bit steep for hobbyists, there’s no question that you get a lot for your money. Capable of performing 40 trillion operations per second (TOPS), NVIDIA estimates the Orin Nano is a staggering 80X as powerful as the previous Nano. It’s a level of performance that, admittedly, not every Hackaday reader needs on their workbench. But the allure of a palm-sized supercomputer is very real, and anyone with an interest in experimenting with machine learning would do well to weigh (literally, and figuratively) the Orin Nano against a desktop computer with a comparable NVIDIA graphics card.

We were provided with one of the very first Jetson Orin Nano Developer Kits before their official unveiling during NVIDIA GTC (GPU Technology Conference), and I’ve spent the last few days getting up close and personal with the hardware and software. After coming to terms with the fact that this tiny board is considerably more powerful than the computer I’m currently writing this on, I’m left excited to see what the community can accomplish with the incredible performance offered by this pint-sized system.

More. More is Good

At first glance, the Jetson Orin Nano Developer Kit looks remarkably like the previous Nano. It seems clear NVIDIA knew they had a winning design, and wisely decided to capitalize on that rather than trying to start over from scratch. It’s an excellent example of taking a good idea and making it better — they simply added more of everything, both inside and out.

Jetson Orin Nano 8GB Developer Kit (Left) vs the Jetson Nano 2GB Developer Kit from 2020 (Right)

The front of the Orin Nano Dev Kit features a DC barrel jack for power (19 V @ 2.4 A), four USB 3.2 Type-A ports, DisplayPort video, gigabit Ethernet, and a USB-C port that the documentation explains is for debug purposes only. The left side features two CSI camera connectors, and on the right, the same 40-pin expansion connector as seen on the previous Nano boards.

Of all these changes, I did find the switch to DisplayPort somewhat annoying. While DP is hardly a rare connector these days, there’s no competing with the ubiquity of HDMI. The return of the DC jack is also somewhat interesting, as its removal and replacement with a USB-C connector was one of the changes NVIDIA made between the original Jetson Nano and the cost-optimized $59 version. As the power requirements of the Orin Nano are within the capability of USB-C Power Delivery, I can only assume some user feedback must have triggered the change back to the more traditional connector.

Note the plastic frame — a welcome improvement over the traditional bare PCB.

Flipping the board over, we can see some more additions. Unlike its predecessors, the Orin Nano Dev Kit gets wireless capability in the form of a AzureWave AW-CB375NF WiFi/Bluetooth card plugged into the board’s M.2 2230 slot, complete with dual PCB antennas. There’s a second M.2 Key M slot for storage expansion, and a Key E slot that the documentation says breaks out PCIe, USB 2.0, UART, I2S, and I2C.

Ludicrous Speed

Plugs and ports are nice, but of course with something like this, the real question is how powerful it is. While the previous Jetson Nano brought a 128-core Maxwell GPU to the party, the new Orin Nano is packing NVIDIA’s Ampere architecture with 1,024 CUDA cores and 32 Tensor cores. That’s in addition to the 6-core ARM Cortex-A78AE CPU and 8 GB of LPDDR5 RAM that’s responsible for running the operating system itself.

The comparisons of the two boards provided by NVIDIA are hilariously one-sided, making it clear these two devices are in very different categories. Accordingly, the company doesn’t even bother to compare the Orin Nano with other SBCs on the market. Probably for good reason — as the previous Jetson Nano (rated at 472 GFLOPs) could already far exceed the raw computational power of the Pi 4 (estimated to be capable of 13.5 GFLOPS), it wouldn’t even be a blip on these charts.

But what do all these numbers mean in the real-world? As a simple test, I re-ran the same live object detection demo used as a benchmark during my hands-on with the 2020 Nano. While the previous board could handle a respectable 25 frames per second (FPS), it notably maxed out the available RAM in the process. In comparison, the Orin Nano screamed through the same demo at 180 FPS while consuming less than half of the available system memory.

Put simply, if you’re doing any kind of machine learning or artificial intelligence project, the move to the Orin Nano represents a generational leap over the previous hardware.

Software: Capable, but Heavy

While you’d be hard pressed to find much fault with the Orin Nano hardware, I did run into some pain points with the software side of things. Nothing that would dissuade me from recommending the product, but still things that I’d like to see improved in the future if possible.

Ultimately, my biggest gripe comes from NVIDIA’s decision to base their customized Linux build on Ubuntu. At the risk of starting a Holy War in the comments, Ubuntu strikes me as a far heavier operating system than you’d want on a SBC designed for peak performance. Indeed, the documentation for the older Nano recommended you kill Ubuntu’s GUI to try and free up RAM. The new Orin doesn’t have that particular problem, but I still didn’t like seeing the operating system eating up precious space on the SD card with snap packages.

As I said in my hands-on with the 2020 Nano, it would be nice if NVIDIA offered a more streamlined operating system for these boards, specifically one that’s better suited to headless operation. As it stands, the software setup is really geared towards the user having a monitor, mouse, and keyboard plugged into the Orin Nano — which obviously isn’t how its going to be operated in the field.

That said, I do appreciate having all of the libraries, tools, and demos required to use the board’s CUDA cores pre-installed and ready to go. Officially this suite is referred to as the JetPack SDK, and it provides everything you need to start writing your own accelerated AI applications. The best part is that the SDK is put together in such a way that code written on one Jetson board should run on all the others, just at different speeds depending on the hardware. So you could start your project on the Orin Nano Dev Kit, but then deploy it on one of the higher-end boards when it came time for production.

If You Need It, It’s Worth It

As I said in the beginning of this hands-on, not everyone is going to need this kind of power. To once again use the object detection demo as an example, your DIY project almost certainly doesn’t need to run at 150+ frames per second. Even with the RAM limitation, one of the older Jetson Nano boards would be more than suitable for identifying squirrels in your backyard.

A look at the official benchmarks provided by NVIDIA even show as much. Depending on the model, the previous Jetson Nano can still pull off more than 30 FPS. If one of those happens to be something you’re interested in playing with, you could save yourself some money by going with the older hardware.

But if you’re more serious with AI software and want a convenient research and experimentation platform that’s strong enough for more complex models, the Jetson Orin Nano Developer Kit is very compelling. While an older gaming PC could potentially crunch more raw data, there’s no beating it in terms of size and energy efficiency, to say nothing of gaining access to NVIDIA’s official development environment — even if it is a bit heftier than I’d like.

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Author Of this post: Tom Nardi

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