Description The NVIDIA Jetson TX2 NX Module is a compact edge AI module for entry-level embedded AI products, real-time inference, smart cameras, factory automation, retail analytics, agriculture systems, and life science devices. Waveshare-19582 is the Jetson TX2 NX module version with NVIDIA Pascal GPU architecture, 256 CUDA cores, 4GB LPDDR4 memory, 16GB eMMC storage, and the 260-pin SO-DIMM edge connector used by the Jetson NX form factor. The NVIDIA Jetson TX2 NX Module delivers up to 1.33 TFLOPS of AI performance while keeping power consumption in the 7.5W to 15W range. For developers building an edge AI module, the NVIDIA Jetson TX2 NX Module provides a balanced mix of GPU acceleration, embedded CPU processing, camera input, PCIe expansion, display support, Ethernet, and NVIDIA JetPack SDK software support. Because Jetson TX2 NX uses the same 69.6mm x 45mm module footprint and 260-pin edge connector family as Jetson Xavier NX style baseboards, it is suitable for compact product designs and faster development on compatible carrier boards. The NVIDIA Jetson TX2 NX Module is a practical edge AI module choice when a project needs power-efficient inference, small size, and NVIDIA JetPack SDK compatibility without moving to a larger Jetson form factor. Use the NVIDIA Jetson TX2 NX Module when a compact edge AI module must combine Pascal GPU acceleration, onboard eMMC storage, camera connectivity, and NVIDIA JetPack SDK workflows in the Jetson NX module footprint. The NVIDIA Jetson TX2 NX Module is especially useful for teams standardizing an edge AI module around Jetson TX2 NX software and compatible carrier hardware. Features NVIDIA Jetson TX2 NX Module: compact AI compute module for embedded and edge AI products. 1.33 TFLOPS AI performance: supports real-time inference, computer vision, and entry-level deep learning workloads. NVIDIA Pascal GPU: 256 NVIDIA CUDA cores accelerate AI compute and image processing. Hybrid CPU complex: dual-core NVIDIA Denver 2 64-bit CPU plus quad-core Arm Cortex-A57 MPCore processor complex. 4GB LPDDR4 memory: 128-bit memory interface with 51.2GB/s bandwidth for embedded AI workloads. Compact NX form factor: 69.6mm x 45mm module with 260-pin SO-DIMM edge connector. Camera and multimedia support: supports MIPI CSI-2 camera input, 4K video encode/decode, and display output for vision systems. NVIDIA JetPack SDK support: enables Jetson Linux, CUDA, inference acceleration, multimedia APIs, and NVIDIA development workflows for the NVIDIA Jetson TX2 NX Module. Edge AI module integration: the NVIDIA Jetson TX2 NX Module gives product teams a small edge AI module with compute, storage, camera, display, and PCIe resources. Specifications SKU Waveshare-19582 Part No. Jetson TX2 NX Product NVIDIA Jetson TX2 NX Module AI Performance 1.33 TFLOPS GPU NVIDIA Pascal architecture with 256 NVIDIA CUDA cores CPU Dual-core NVIDIA Denver 2 64-bit CPU and quad-core Arm Cortex-A57 MPCore processor complex Memory 4GB 128-bit LPDDR4, 51.2GB/s Storage 16GB eMMC 5.1 Power 7.5W | 15W PCIe 1 x1 plus 1 x2 PCIe Gen2, total 30GT/s CSI Camera Up to 5 cameras, 12 via virtual channels; 12 lanes MIPI CSI-2, 3x4 or 5x2; D-PHY 1.2 up to 30Gbps Video Encode 1x 4K60, 3x 4K30, 4x 1080p60, 8x 1080p30 H.265; 1x 4K60, 3x 4K30, 7x 1080p60, 14x 1080p30 H.264 Video Decode 2x 4K60, 4x 4K30, 7x 1080p60, 14x 1080p30 H.265 and H.264 Display 2 multi-mode DP 1.2 / eDP 1.4 / HDMI 2.0; 1x2 DSI at 1.5Gbps per lane Networking 10/100/1000 BASE-T Ethernet Mechanical 69.6mm x 45mm Connector 260-pin SO-DIMM edge connector Applications Entry-level edge AI module designs for compact embedded products using the NVIDIA Jetson TX2 NX Module. Smart cameras, AI vision systems, and real-time video analytics. Factory robots, industrial inspection, and low-latency machine vision. Retail analytics, agriculture monitoring, life science devices, and intelligent IoT systems. Carrier-board development using compatible Jetson NX form factor baseboards. AI model deployment with NVIDIA JetPack SDK, Jetson Linux, CUDA, and multimedia APIs. Product List 1 Jetson TX2 NX Module Other Content Form factor note: The NVIDIA Jetson TX2 NX Module uses a 69.6mm x 45mm module size and 260-pin SO-DIMM edge connector. Confirm carrier board compatibility, thermal design, power budget, camera lanes, and software version before integration. Performance note: The NVIDIA Jetson TX2 NX Module is designed for compact, power-efficient edge AI module designs. It is positioned for embedded AI products that need GPU inference acceleration and a smaller footprint than larger Jetson TX2 or AGX modules. Software note: NVIDIA JetPack SDK support helps developers use Jetson Linux, CUDA, accelerated multimedia, and AI deployment workflows on compatible NVIDIA Jetson TX2 NX Module systems. Selection note: Choose the NVIDIA Jetson TX2 NX Module when an edge AI module needs Pascal GPU acceleration, 4GB memory, 16GB eMMC, 7.5W to 15W operation, and the Jetson NX mechanical form factor. Resources Waveshare Jetson TX2 NX Wiki NVIDIA JetPack SDK NVIDIA Jetson Download Center NVIDIA Jetson Ecosystem FAQ What is the NVIDIA Jetson TX2 NX Module used for? The NVIDIA Jetson TX2 NX Module is used for compact edge AI module designs, smart cameras, industrial vision, robotics, and embedded inference products. Is Waveshare-19582 the Jetson TX2 NX module? Yes. Waveshare-19582 refers to the NVIDIA Jetson TX2 NX Module with 4GB LPDDR4 memory, 16GB eMMC, and the 260-pin SO-DIMM edge connector. Does Jetson TX2 NX support NVIDIA JetPack SDK? Yes. The NVIDIA Jetson TX2 NX Module works with NVIDIA JetPack SDK workflows for Jetson Linux, CUDA, accelerated multimedia, and AI deployment on compatible carrier boards. Can this module be used with Jetson Xavier NX carrier boards? Jetson TX2 NX is designed around the compact NX module form factor and is described by Waveshare as compatible with the baseboard of the Jetson Xavier NX Developer Kit for quick development and debugging. Always verify carrier board pinout, power, thermal, and software compatibility before use. Is this a complete developer kit? No. It is a module. A compatible carrier board, power supply, cooling solution, storage or software setup, and peripherals may be required for development or deployment.
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