What is NVIDIA Jetson AGX Xavier Developer Kit (32GB) price and who should buy this? The Jetson Xavier Developer Kit with Jetson Xavier module and reference carrier board is the fastest way to start prototyping with robots, drones and other autonomous machines. It’s also supported by the NVIDIA Deep Stream SDK, which delivers a complete toolkit for real-time situational awareness through intelligent video analytics (IVA)and NVIDIA Isaac SDK, which delivers a software toolkit for robot development. These helps boost performance and accelerate software development, while reducing development cost and effort.
What specs you are getting at NVIDIA Jetson AGX Xavier Developer Kit (32GB) price?
JETSON AGX XAVIER
- Storage 32GB eMMC 5.1
- DL Accelerator (2x) NVDLA Engines
- GPU 512-core Volta GPU with Tensor Cores
- CPU 8-core ARM v8.2 64-bit CPU, 8MB L2 + 4MB L3
- Memory 32GB 256-Bit LPDDR4x | 137GB/s
- Size 105 mm x 105 mm x 65 mm
- Deployment Module (Jetson AGX Xavier)
- Vision Accelerator 7-way VLIW Vision Processor
- Encoder/Decoder (2x) 4Kp60 | HEVC/(2x) 4Kp60 | 12-Bit Support
DEVELOPER KIT I/OS
- M.2 Key M NVMe
- M.2 Key E PCIe x1 + USB 2.0 + UART (for Wi-Fi/LTE) / I2S / PCM
- 40-Pin Header UART + SPI + CAN + I2C + I2S + DMIC + GPIOs
- Developer Kit I/Os Jetson AGX Xavier Module Interface
- USB-C 2x USB 3.1, DP (Optional), PD (Optional) Close-System Debug and Flashing Support on 1 Port
- Camera Connector (16x) CSI-2 Lanes
- PCIe X16 x8 PCIe Gen4/x8 SLVS-EC
- RJ45 Gigabit Ethernet
- HD Audio Header High-Definition Audio
- eSATAp + USB3.0 Type A SATA Through PCIe x1 Bridge (PD + Data for 2.5-inch SATA) + USB 3.0
- HDMI Type A HDMI 2.0
- uSD/UFS Card Socket SD/UFS
NVIDIA Jetson AGX Xavier Developer Kit (32GB) price
The immense power packed in the Jetson AGX Xavier does mark a steep price increase at $2500 USD for the entire developer kit or currently the NVIDIA Developer Program offers the developer kit at $1,299 USD. The new AGX Xavier module is $1,399 or $1,099 in quantities of 1k+ units.
The previous-generation Jetson TX2 is still available at around $550 USD with its dual Denver ARMv8 CPU cores and four Cortex-A57 cores paired with a NVIDIA Pascal GPU sporting 256 CUDA cores, 8GB of LPDDR4 memory, and no deep learning accelerators or tensor cores.
Who is this for
Nvidia’s Jetson modules are primarily designed for embedded applications, meaning the SoM will be embedded into a specific product. Anything from robots, drones, machine vision systems, high-resolution sensor arrays, video analytics, and autonomous machines can benefit from the machine learning performance, small form factor, and lower power requirements of the Xavier NX.
NVIDIA Jetson AGX Xavier Developer Kit (32GB) review
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! 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 NVIDIA Jetson AGX Xavier Developer Kit has a 384 core GPU based on the Volta architecture. Each generation of GPU from Nvidia is based on a new microarchitecture design. This central design is then used to create different GPUs (with different core counts, and so on) for that generation. The Volta architecture is aimed at the datacenter and at AI applications. It can be found in PC graphic cards like the Nvidia Titan V.
All of Nvidia’s Jetson boards come with excellent documentation and example projects. Because they all use the same ecosystem and software (JetPack etc) then the examples work equally as well on the Jetson Nano or on the Jetson Xavier NX. A great place to start is the Hello AI World example. It is simple to download and compile, and in just a few minutes, you will have an AI demo up and running for image classification, object detection, and semantic segmentation, all using pre-trained models.
The Hello AI World demo gives you a set of tools to play around with including an image classifier, and an object detection program. These tools can either process photos or use a live camera feed. I fished out a picture of a Jellyfish (pun intended) from my visit to the Monterey Bay Aquarium in 2018 and asked the image classifier to label it.
The ability to run self-contained micro-services in a pre-defined environment. However this concept isn’t limited to huge servers in a data center, it can also be applied to smaller devices. Container software like Docker runs on Arm-based systems, including the Raspberry Pi and the Xavier NX. The machine learning demo above is actually four separate containers running in parallel, on the development board.
The 8GB of RAM and support for M.2 NVMe makes this a significant upgrade to the Jetson Nano, but the real upgrade is in the processing power. Compared to the Jetson Nano, the NVIDIA Jetson AGX Xavier Developer Kit is anywhere between two to seven times faster, depending on the application.
The device can be configured in two power envelopes: 10W and 15W. Measured at the wall, however, its peak power draw during the demo and configured for the latter power envelope reached 24W — far lower than a desktop computer and graphics card performing the same tasks, but a high figure for an edge device.
Setting up the Jetson AGX Xavier Developer Kit was as easy as previous Jetson boards thanks to NVIDIA’s JetPack software. The current Linux4Tegra sample file-system for the AGX Xavier is based on the current Ubuntu 18.04 LTS, which is nice compared to the now dated Ubuntu 16.04 packages currently used by older versions of Jetson.
NVIDIA Jetson AGX Xavier Developer Kit (32GB) (price) Performance review
Performance and form factor are essential for embedded projects, but so is power usage. The Jetson Xavier NX delivers up to 21 Trillions Operations per Second (TOPS) while using up to 15 watts of power. When needed the board can be set into a 10W mode. Both power modes can be tweaked depending on how much CPU performance you need compared to the GPU performance. For example, you could run just two CPU cores at 1.9GHz and the GPU at 1.1GHz or alternatively you could use four CPU cores @1.2GHz and clock the GPU at 800Mhz. The level of control is exceptional.
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