{"product_id":"nvidia-jetson-orin-nx-16gb-ai-computing-module-157-tops-edge-ai-som","title":"NVIDIA Jetson Orin NX 16GB AI Computing Module 157 TOPS Edge AI SOM","description":"\u003cdiv\u003e\n\u003ch1\u003eNVIDIA Jetson Orin NX 16GB AI Computing Module\u003c\/h1\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Jetson Orin NX 16GB\u003c\/strong\u003e delivers supercomputer-class AI performance to the edge in a compact \u003cstrong\u003esystem-on-module (SOM)\u003c\/strong\u003e smaller than a credit card. With up to \u003cstrong\u003e157 TOPS of AI performance\u003c\/strong\u003e in Super Mode and \u003cstrong\u003e16GB LPDDR5 memory\u003c\/strong\u003e, this module powers advanced robotics, autonomous machines, industrial vision systems, and edge AI applications where size, weight, and power are critical constraints.\u003c\/p\u003e\n\n\u003ch2\u003eWhy Choose the Jetson Orin NX 16GB?\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eUp to 157 TOPS AI performance\u003c\/strong\u003e: Run multiple concurrent AI inference pipelines with NVIDIA Ampere GPU architecture featuring 1024 CUDA cores and 32 Tensor Cores\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompact 69.6 x 45mm form factor\u003c\/strong\u003e: Smallest Jetson form factor with 260-pin SO-DIMM connector for easy integration into space-constrained designs\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFlexible power options\u003c\/strong\u003e: Configurable between 10W and 40W to match your application requirements\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e8-core Arm Cortex-A78AE CPU\u003c\/strong\u003e: High-performance 64-bit processing with 102 GB\/s memory bandwidth\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e5x performance boost\u003c\/strong\u003e: Up to 5 times the performance of Jetson Xavier NX and 3 times faster than Jetson AGX Xavier\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSuper Mode upgrade\u003c\/strong\u003e: Software-upgradable to 157 TOPS via JetPack 6.2, delivering 70% performance improvement\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003ePowerful AI Architecture for Edge Computing\u003c\/h2\u003e\n\u003cp\u003eBuilt on the \u003cstrong\u003eNVIDIA Ampere architecture\u003c\/strong\u003e, the Jetson Orin NX 16GB module processes data from multiple high-resolution sensors simultaneously. The integrated \u003cstrong\u003e1024-core GPU with 32 Tensor Cores\u003c\/strong\u003e enables real-time deep learning inference, computer vision, and multi-sensor fusion. With \u003cstrong\u003e16GB 128-bit LPDDR5 DRAM running at 3200 MHz\u003c\/strong\u003e, this module handles demanding AI workloads including natural language processing, 3D perception, and autonomous navigation.\u003c\/p\u003e\n\n\u003ch2\u003eIdeal Applications \u0026amp; Use Cases\u003c\/h2\u003e\n\u003cp\u003eThe Jetson Orin NX 16GB is the go-to choice for production AI systems in robotics, drones, autonomous vehicles, industrial inspection, smart city infrastructure, medical instruments, and intelligent gateways. Its credit-card size and low power draw make it perfect for \u003cstrong\u003eUAVs, handheld devices, robotic systems\u003c\/strong\u003e, and installations where the larger AGX Orin is impractical.\u003c\/p\u003e\n\n\u003ch2\u003eDeveloper-Friendly Software Stack\u003c\/h2\u003e\n\u003cp\u003eFull support for \u003cstrong\u003eNVIDIA JetPack SDK\u003c\/strong\u003e, Jetson Linux, CUDA 11.4+, Isaac ROS, and all major AI frameworks including TensorFlow, PyTorch, and ONNX Runtime. Native \u003cstrong\u003eROS 2 Humble support\u003c\/strong\u003e on Ubuntu 22.04 with JetPack 6 accelerates robotics development.\u003c\/p\u003e\n\n\u003ch2\u003eHigh-Speed Connectivity \u0026amp; Interfaces\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003ePCIe 4.0 x4 interface for NVMe storage and expansion\u003c\/li\u003e\n\u003cli\u003eMultiple CSI camera interfaces for multi-camera vision systems\u003c\/li\u003e\n\u003cli\u003eUSB 3.2, DisplayPort, Gigabit Ethernet support via carrier board\u003c\/li\u003e\n\u003cli\u003eGPIO, I2C, SPI, UART for sensor and peripheral integration\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWhat's in the Box\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNVIDIA Jetson Orin NX 16GB Module (bare SOM - requires compatible carrier board)\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eFull Specifications\u003c\/h2\u003e\n\n\u003ch3\u003eCompute Performance\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 157 TOPS (Sparse INT8) in Super Mode, 100 TOPS standard mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ampere with 1024 CUDA cores and 32 Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e8-core Arm Cortex-A78AE v8.2 64-bit (up to 2.0 GHz)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeep Learning Accelerators\u003c\/td\u003e\n\u003ctd\u003e2x NVDLA v2 engines\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVision Accelerators\u003c\/td\u003e\n\u003ctd\u003e1x PVA v2 (Programmable Vision Accelerator)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003eMemory \u0026amp; Storage\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e16GB 128-bit LPDDR5 (3200 MHz, 102 GB\/s bandwidth)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage Support\u003c\/td\u003e\n\u003ctd\u003eNVMe (via PCIe), eMMC options available\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003eVideo Encode\/Decode\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eVideo Encoder\u003c\/td\u003e\n\u003ctd\u003e2x 4K60 (H.265, H.264, AV1)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVideo Decoder\u003c\/td\u003e\n\u003ctd\u003e4K120 (H.265, H.264, VP9, AV1)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003ePhysical Specifications\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e69.6mm × 45mm system-on-module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConnector\u003c\/td\u003e\n\u003ctd\u003e260-pin SO-DIMM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApprox. 30g (module only)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003ePower\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003e10W to 40W (configurable power modes)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStandard Mode\u003c\/td\u003e\n\u003ctd\u003e10W to 25W (100 TOPS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuper Mode\u003c\/td\u003e\n\u003ctd\u003e10W to 40W (157 TOPS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003eSoftware Support\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eJetson Linux (based on Ubuntu 22.04 LTS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eJetPack SDK\u003c\/td\u003e\n\u003ctd\u003eJetPack 6.2+ with Super Mode support\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eTensorFlow, PyTorch, ONNX Runtime, TensorRT\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRobotics\u003c\/td\u003e\n\u003ctd\u003eROS 2 Humble, Isaac ROS, Isaac Sim\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA\u003c\/td\u003e\n\u003ctd\u003eCUDA 11.4+, cuDNN, OpenCV, VPI\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003eGraphics \u0026amp; Display\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU APIs\u003c\/td\u003e\n\u003ctd\u003eOpenGL 4.6, OpenGL ES 3.2, Vulkan 1.3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Support\u003c\/td\u003e\n\u003ctd\u003eUp to 2x 4K displays (via carrier board)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003ch3\u003eWhat's in the Box\u003c\/h3\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eContents\u003c\/td\u003e\n\u003ctd\u003eJetson Orin NX 16GB bare module (carrier board sold separately)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\n\u003cp\u003e\u003cem\u003eSpecifications are sourced from the official NVIDIA product page and may be updated. The Jetson Orin NX 16GB is a system-on-module that requires a compatible carrier board for operation. Not for use in military, aerospace, nuclear, or supercomputing applications.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":44324768088142,"sku":"PA-NVIDIAJETSONORINN","price":1500.0,"currency_code":"AUD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0676\/3134\/9838\/files\/jetson-orin-nx-16gb-module.png?v=1789258071","url":"https:\/\/projectorsaustralia.com.au\/products\/nvidia-jetson-orin-nx-16gb-ai-computing-module-157-tops-edge-ai-som","provider":"Projectors Australia","version":"1.0","type":"link"}