Here’s a comparison of the Jetson AGX Xavier with the Jetson AGX Orin: Performance on Vision AI and Conversational AI Models Courtesy NVIDIA NVIDIA provides Deep Learning benchmark tests for comparing performance of different devices. The wire controls the fan on the heat sink: Module Right, Carrier Board Left Jetson Module Heat Sink Module Left, Carrier Board Right Benchmarks Be careful, as the module is attached to the carrier board with a small wire. By gently rocking the Jetson Module, we can detach the module from the carrier board. The Jetson AGX Module connects to the carrier board with a 699 pin connector. You can see the stack top to bottom, with the carrier board (on a plastic stand), Jetson AGX Orin module, and heat sink with fan: Dev Kit, Cover Removed Dev Kit, Cover Removed, Opposite side Removing the screws, we can take off the case cover to expose the Jetson AGX Orin module. The screws are easily accessible on the underside of the Developer Kit. There are 4 screws that hold the Developer Kit together. Let’s take a look at what’s inside after removing the case cover. Be careful when removing the cover as the antennas are connected to a WiFi card on the carrier board: Secret! PCIe connector, antennas Removing the cover exposes a PCIe connector. This marks a cover held on to the Developer Kit with magnets. I modified mine to fix that by adding googly eyes. One side holds a secret! From the factory, the Jetson AGX Orin is beautiful. Here are the four different different sides, which hold the headers and jacks mentioned above: People connect their peripherals to connectors on the sides of the Developer Kit, which connect to the carrier board. The carrier board provides access to the input and output capabilities of the kit, along with supplying the module with power. NVIDIA makes the design for the Jetson AGX Developer Kit Carrier Board freely available. The second part of the Developer Kit is the carrier board. Jetson AGX Orin Developer Kit Carrier Board For many developers, they will replace this particular thermal solution to meet requirements in their own designs. The heat sink/fan is referred to as a thermal solution. On the Developer Kit, attached to the Jetson AGX Orin module is a heat sink with integrated fan. This assembly is in a metal case which also thermally connects to the SoC: Jetson AGX Orin SoC Jetson AGX Orin Carrier Board Jetson AGX Orin Module The SoC attaches to a PCB which hold the main memory and eMMC, along with support circuitry and a 699 pin interface connector. The SoC is the brains of the Jetson and contains all the compute elements. The first is the Jetson Orin System On a Chip ( SoC). The AGX Orin Module consists of three main parts. The second part is a carrier board which provides input/output, memory and electrical connections to the module. The first is the Jetson AGX Orin Module, which is a compute module. You can think of the Jetson AGX Orin Developer Kit as two parts. Specsīefore we get much further, the price at introduction is $1999 USD. There is also a group of frameworks which leverage these new under pinnings we will talk about in upcoming articles. Latest compute stack with latest versions of CUDA 11 and TensorRT 8.The software upgrades will run on both the Orin series and the Xavier series of Jetsons. The software side is another major upgrade which we will soon be covering. We’ll cover mostly the AGX Orin hardware in this article. Here, we’ll cover some of the highlights, just to wet our appetite for upcoming development. Or, similarly, you might be able to either save more power, or get better precision, if you’re currently at 240 Hz but are OK with 60 Hz (or 10 Hz, for that matter.The Jetson AGX Orin is the next evolution of the the Jetson product line. The difference between 60 Hz and 240 Hz inference rate is unlikely to be particularly important to many applications, but the cost to go from one to the other might be significant. Then, you pick the solution that seems to be the best trade-off, given your needs.Īlso note that there are diminishing returns in performance. Then, you benchmark a few different solutions that seem to fit within the envelope. In all engineering, to solve a particular problem, you figure out how important each factor is: cost, power, time-to-market, precision, performance, etc. The Jetson Nano is now a very old GPU architecture, the AGX Xavier is a somewhat old GPU architecture, and the RTX 30 series is the currently newest available GPU architecture. This isn’t super surprising, both because of the significant power and cost difference, and because of the technology level difference. The inference performance difference between a top-of-the-line desktop GPU, and a Jetson AGX Xavier, is likely to be on the order of 20x different, similar to how the inference performance difference between the AGX Xavier and the Jetson Nano is another 20x difference.
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