Description
Reviews
| Brand | - |
| Category | Robotics / STEM Teaching Tools / Development, Learning, Evaluation & Industrial Control Boards |
| Origin | - |
| Processor | NVIDIA Carmel ARMv8.2 64-bit CPU |
| GPU Architecture | 384-core NVIDIA Volta GPU with 48 Tensor Cores |
| AI Performance | Up to 21 TOPS (Trillion Operations Per Second) |
| Memory | 8GB or 16GB 128-bit LPDDR4x |
| Storage Interface | M.2 Key M for NVMe SSD (Card not included) |
| Connectivity | Gigabit Ethernet, Dual-band Wi-Fi, Bluetooth 5.0 |
| Video Output | HDMI 2.0b and DisplayPort 1.4 via USB-C |
| Camera Interfaces | 2x MIPI CSI-2 lanes (supports up to 6 cameras) |
| Power Input | 5V DC via USB Type-C or GPIO header |
| Form Factor | Compact 70mm x 45mm module on carrier board |
Anonymous
Mar 26, 2026
Rating: 5/5
Excellent Build Quality
The Waveshare version feels very sturdy. The heatsink design keeps temperatures manageable even under sustained load. Highly recommend for industrial control applications.
Anonymous
Mar 25, 2026
Rating: 4/5
Great performance, steep learning curve
The hardware is fantastic, but setting up the JetPack SDK took some time if you are new to the NVIDIA ecosystem. Once configured, the inference speed is unmatched in this price range.
Anonymous
Mar 17, 2026
Rating: 5/5
Ideal for Autonomous Robots
We integrated this into our mobile robot platform. The low power consumption combined with high compute capability allows for hours of operation on battery power while processing LIDAR and camera data simultaneously.
Anonymous
Mar 16, 2026
Rating: 5/5
Best value for deep learning dev
If you need to deploy TensorFlow or PyTorch models on the edge, this is the board to get. The community support is huge, and finding tutorials for this specific configuration is easy.
Anonymous
Mar 11, 2026
Rating: 4/5
Solid upgrade from Nano
Coming from the Jetson Nano, the performance jump is massive. The only downside is that it runs a bit warm without active cooling, so I added a small fan which solved the issue completely.
Anonymous
Mar 02, 2026
Rating: 5/5
Perfect for Edge AI Prototyping
This board is incredibly powerful for its size. I was able to run YOLOv5 object detection at over 30 FPS with ease. The Waveshare carrier board provides excellent pinout access for GPIO projects.
Q: Does this kit come with an SD card or eMMC storage pre-installed?
A: No, this development kit does not include storage media. You will need to purchase a high-endurance microSD card (minimum 32GB recommended) or an M.2 NVMe SSD separately to install the operating system.
Q: What version of Ubuntu and CUDA does this support out of the box?
A: It supports NVIDIA JetPack SDK, which typically includes Ubuntu 18.04 or 20.04 (depending on the JetPack version flashed), along with CUDA, cuDNN, and TensorRT. You download the image from the NVIDIA developer site.
Q: Can I connect multiple Raspberry Pi cameras to this board?
A: Yes, the board supports up to 6 cameras via its dual MIPI CSI-2 interfaces. However, ensure you use compatible camera modules designed for the Jetson ecosystem or adapters that support the specific pinout.
Q: Is the power supply included in the box?
A: The package includes the carrier board and the Jetson Xavier NX module, but a dedicated power adapter is not included. You can power it via a standard 5V/4A USB-C charger or through the GPIO header.
Q: Is there active cooling required for continuous inference tasks?
A: While the passive heatsink works for intermittent tasks, we strongly recommend adding an active fan for continuous heavy workloads like video analytics or training to prevent thermal throttling.
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