company blog about Mingjiada Electronics Supply [NVIDIA] Jetson Orin Nano Series System On Modules

Mingjiada Electronics Supply [NVIDIA] Jetson Orin Nano Series System On Modules

Mingjiada Electronics, a specialist supply chain service provider with over 20 years' experience in the electronic components sector, has formally commenced supply of the NVIDIA Jetson Orin Nano series System on Modules (SOM). Leveraging its globally distributed supply chain network, substantial original manufacturer inventory, and efficient delivery capabilities, the company delivers high-quality, cost-effective core modules for customers in edge AI, embedded computing, and smart device development. NVIDIA Jetson Orin Nano Series System-on-Modules (SOM). This initiative delivers high-quality, cost-effective core modules and comprehensive supply chain solutions to clients in edge AI, embedded computing, and smart device development, accelerating their product deployment.

The NVIDIA Jetson Orin Nano series SOM is a system-on-module specifically engineered for entry-level edge AI and high-performance embedded computing scenarios. Its compact form factor, low power consumption, and formidable performance make it an ideal core component for small autonomous machines and edge inference devices. Delivering a quantum leap in performance over its predecessor, it sets a new benchmark for diverse edge intelligence applications.

This SOM series offers two memory configurations: 4GB and 8GB. Its core performance is outstanding: featuring a 6-core Cortex-A78AE v8.2 64-bit CPU paired with an NVIDIA Ampere architecture GPU. The 4GB variant incorporates a 512-core GPU with 16 Tensor Cores, while the 8GB variant upgrades to a 1024-core GPU with 32 Tensor Cores, delivering computational power ranging from 20 TOPS to 40 TOPS. Upgrading to the SUPER variant further boosts computational power to 34 TOPS (4GB SUPER) and 67 TOPS (8GB SUPER), enabling efficient support for multiple concurrent AI inference pipelines and smooth operation of complex deep learning models such as real-time object recognition models like YOLOv5.

In terms of hardware configuration and compatibility, the Jetson Orin Nano series SOM offers exceptional flexibility: graphics memory utilises high-speed LPDDR5, with bandwidth ranging from 32GB/s to 102GB/s depending on the version, supporting external NVMe storage expansion to meet massive data storage demands; It features a rich array of high-speed interfaces, including PCIe Gen3, USB 3.2, and HDMI 2.0a/b, enabling straightforward connection to diverse sensors such as LiDAR and IP cameras to accommodate multi-scenario hardware expansion requirements; Compact form factor utilising a 260-pin SO-DIMM package facilitates integration into diverse compact devices. Power consumption is dynamically adjustable per application: the 4GB variant operates within 7W–10W, the 8GB version within 7W–15W, while the SUPER edition supports multi-stage power regulation at 7W, 15W, and 25W, achieving precise performance-power balance.

Furthermore, this SOM series supports the NVIDIA JetPack operating system, offering compatibility with multiple development environments and programming languages. It can be upgraded to the SUPER version via software updates, delivering up to 70% performance enhancement while significantly reducing developer onboarding complexity and R&D costs. Its wide operating temperature range and shock/vibration resistance make it suitable for demanding applications such as industrial automation and automotive electronics. Widely deployed in computer vision, autonomous robot navigation, industrial vision inspection, and intelligent security edge analytics, it delivers robust computational power for edge AI device development.

Mingjiada Electronics maintains long-term supply of NVIDIA Jetson Orin Nano Series System on Modules (SOM). For further product information regarding NVIDIA System on Modules (SOM) or to request samples, please visit the Mingjiada Electronics official website (https://www.integrated-ic.com/) for comprehensive supply details.