{"product_id":"nvidia-jetson-xavier-nx-module","title":"NVIDIA Jetson Xavier NX Module","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;color:#e0e0e0;\"\u003eNVIDIA Jetson Xavier NX — 21 TOPS Edge AI — 384-Core Volta GPU — 8GB \u0026amp; 16GB LPDDR4x\u003c\/h2\u003e\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe \u003cstrong\u003eNVIDIA Jetson Xavier NX Module\u003c\/strong\u003e is a production-ready AI System-on-Module (SoM) that delivers server-class Xavier SoC performance in a form factor smaller than a credit card — 69.6 × 45 mm. Available in \u003cstrong\u003e8GB and 16GB LPDDR4x configurations\u003c\/strong\u003e, it connects to any Xavier NX-compatible carrier board via the standard 260-pin SO-DIMM interface, giving embedded engineers, OEMs, and research teams a proven path to full-AI edge systems with up to 21 TOPS, dual NVDLA engines, hardware-accelerated video encode\/decode, and multi-camera sensor fusion.\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 10px;color:#e0e0e0;\"\u003eKey Highlights\u003c\/h3\u003e\n\u003cul style=\"margin:0 0 20px;padding-left:22px;line-height:1.6;list-style-position:outside;color:#e0e0e0;\"\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e21 TOPS AI Performance at the Edge\u003c\/strong\u003e — The Xavier NX delivers 21 TOPS of accelerated computing at 15W or 20W, and up to 14 TOPS at 10W — enabling real-time parallel neural network inference across object detection, segmentation, and video analytics entirely without cloud dependency.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eNVIDIA Volta GPU with 48 Tensor Cores\u003c\/strong\u003e — 384 CUDA cores and 48 Tensor Cores on the Volta architecture provide hardware-accelerated CUDA inference and TensorRT-optimised throughput on INT8 and FP16 workloads — purpose-built for production AI pipelines.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e6-Core Carmel ARM CPU\u003c\/strong\u003e — The 6-core NVIDIA Carmel ARMv8.2 64-bit CPU with 6MB L2 + 4MB L3 cache runs Linux OS management, peripheral I\/O, and application logic in parallel with GPU inference — without resource contention or scheduling delays.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDual NVDLA Deep Learning Accelerators\u003c\/strong\u003e — Two dedicated NVDLA engines offload specific inference workloads entirely from the GPU, enabling simultaneous multi-model execution where one network runs on NVDLA while another occupies the Volta GPU — maximising hardware utilisation per watt.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHardware Video Encode \u0026amp; Decode\u003c\/strong\u003e — Dedicated NVENC and NVDEC engines handle up to 2× 4K60 H.265 encoding and 2× 8K30 decoding simultaneously — freeing CPU and GPU entirely for AI inference while multi-stream video pipelines run in dedicated silicon.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMulti-Camera CSI Pipeline\u003c\/strong\u003e — Up to 6 simultaneous cameras (expandable to 24 via virtual channels) over 14-lane MIPI CSI-2 with D-PHY 1.2 at up to 30 Gbps aggregate bandwidth — purpose-built for surround-view robotics, multi-sensor inspection, and autonomous machine vision.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHigh-Speed PCIe Gen4 Expansion\u003c\/strong\u003e — One PCIe Gen3 ×1 plus one PCIe Gen4 ×4 lane (144 GT\/s total) allow direct attachment of NVMe SSDs, Wi-Fi 6 adapters, FPGA co-processors, and AI accelerator cards with no USB overhead or bandwidth bottleneck.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eOnboard 16GB eMMC 5.1 Storage\u003c\/strong\u003e — Built-in eMMC means the module boots without any external SSD or SD card — simplifying BOM, reducing carrier board complexity, and enabling clean production deployments with a single solid-state storage solution.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eConfigurable Power Envelopes\u003c\/strong\u003e — Selectable 10W, 15W, and 20W power modes let you balance peak AI throughput against thermal budget — critical for passively cooled enclosures, battery-powered field systems, and fanless industrial designs.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eFull JetPack \u0026amp; CUDA Ecosystem\u003c\/strong\u003e — Production-ready support for CUDA, TensorRT, cuDNN, PyTorch, TensorFlow, and NVIDIA JetPack SDK means existing AI models deploy with minimal porting effort — cloud-native container support further accelerates edge deployment pipelines.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 10px;color:#e0e0e0;\"\u003eTechnical Specifications\u003c\/h3\u003e\n\u003cdiv style=\"width:100%;overflow-x:auto;margin:0 0 24px;\"\u003e\n  \u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;min-width:460px;border:0;\"\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eSpecification\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eXavier NX 8GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eXavier NX 16GB\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eAI Performance\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e21 TOPS\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eGPU\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e384-core NVIDIA Volta GPU — 48 Tensor Cores\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eCPU\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e6-core NVIDIA Carmel ARMv8.2 64-bit — 6MB L2 + 4MB L3\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMemory\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e8GB 128-bit LPDDR4x — 59.7 GB\/s\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e16GB 128-bit LPDDR4x — 59.7 GB\/s\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eStorage\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e16GB eMMC 5.1 (onboard)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eDL Accelerator\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× NVDLA Engines\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eVision Accelerator\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e7-Way VLIW Vision Processor\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003ePower\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e10W | 15W | 20W\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003ePCIe\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e1×1 PCIe Gen3 + 1×4 PCIe Gen4 — 144 GT\/s total\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eCSI Camera\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003eUp to 6 cameras (24 via virtual channels) — 14-lane MIPI CSI-2, D-PHY 1.2 up to 30 Gbps\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eVideo Encode\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× 4K60 | 4× 4K30 | 10× 1080p60 | 22× 1080p30 (H.265)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eVideo Decode\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× 8K30 | 6× 4K60 | 12× 4K30 | 22× 1080p60 | 44× 1080p30 (H.265)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eDisplay\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× multi-mode DP 1.4 \/ eDP 1.4 \/ HDMI 2.0\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eNetworking\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e10\/100\/1000 BASE-T Ethernet\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMechanical\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e69.6 × 45 mm — 260-pin SO-DIMM connector\u003c\/td\u003e\n    \u003c\/tr\u003e\n  \u003c\/table\u003e\n\u003c\/div\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 10px;color:#e0e0e0;\"\u003eWhich Jetson Xavier NX Is Right for You?\u003c\/h3\u003e\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eBoth Xavier NX variants are built on the same Xavier SoC and deliver identical 21 TOPS AI performance — the decision is purely about memory headroom for your neural network pipeline. The 8GB module comfortably handles single or dual-model workloads and most standard embedded AI deployments. Choose the 16GB module when running large transformer models, concurrent multi-model inference, or high-resolution multi-stream video analytics where 8GB becomes the bottleneck — memory is soldered and cannot be upgraded after purchase.\u003c\/p\u003e\n\u003cdiv style=\"width:100%;overflow-x:auto;margin:0 0 24px;\"\u003e\n  \u003ctable style=\"width:100%;border-collapse:collapse;font-size:14px;min-width:460px;border:0;\"\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eFeature\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eXavier NX 8GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eXavier NX 16GB\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eRAM\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e8GB 128-bit LPDDR4x\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e16GB 128-bit LPDDR4x\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMemory Bandwidth\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e59.7 GB\/s (identical across both variants)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eAI Performance\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e21 TOPS (identical across both variants)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eGPU \u0026amp; CPU\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003eIdentical across both variants\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003ePower Range\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e10W | 15W | 20W (identical across both variants)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eBest For\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eSingle\/dual-model inference, standard multi-camera deployments, most embedded AI applications\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eLarge models, multi-model concurrent inference, 4K multi-stream analytics, future-proofed OEM designs\u003c\/td\u003e\n    \u003c\/tr\u003e\n  \u003c\/table\u003e\n\u003c\/div\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 10px;color:#e0e0e0;\"\u003eCommon Applications \u0026amp; Use Cases\u003c\/h3\u003e\n\u003cul style=\"margin:0 0 20px;padding-left:22px;line-height:1.6;list-style-position:outside;color:#e0e0e0;\"\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eCommercial Robotics \u0026amp; Autonomous Machines\u003c\/strong\u003e — Run simultaneous perception, planning, and navigation models on a single Xavier NX module using the Volta GPU, dual NVDLA engines, and multi-camera CSI pipeline — purpose-built for the demanding real-time workloads of mobile robots and autonomous guided vehicles.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAutomated Optical Inspection (AOI)\u003c\/strong\u003e — High-throughput defect detection on industrial production lines benefits directly from hardware-accelerated 6-camera CSI capture, TensorRT inference on Volta, and dedicated NVDLA offload — delivering sub-millisecond decision latency without cloud round-trips.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMedical Instruments \u0026amp; Diagnostic Imaging\u003c\/strong\u003e — Certified production-ready hardware with defined power envelopes and consistent compute performance makes the Xavier NX suitable for AI-assisted imaging devices, surgical robotics, and bedside diagnostic tools requiring regulatory-grade reliability.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart Cameras \u0026amp; Intelligent Vision Systems\u003c\/strong\u003e — Embedded AI directly in the camera node eliminates central compute bottlenecks — the Xavier NX processes inference, encodes video in hardware, and communicates results over Ethernet or PCIe in a single compact thermal envelope.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHigh-Resolution Multi-Sensor Fusion\u003c\/strong\u003e — Up to 6 simultaneous high-resolution CSI cameras, dual NVDLA engines, and the Volta GPU enable complex multi-modal sensor fusion tasks in autonomous vehicles, drones, and advanced driver-assistance systems (ADAS).\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart Factory \u0026amp; Industry 4.0 Systems\u003c\/strong\u003e — Deploy predictive maintenance, worker safety monitoring, and real-time quality control AI directly on the factory floor using the Xavier NX's sealed SO-DIMM form factor, fanless-friendly power modes, and industrial I\/O interfaces.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eEdge Video Analytics at Scale\u003c\/strong\u003e — Simultaneously decode up to 44× 1080p30 streams in hardware and run AI inference without GPU involvement in decoding — making the Xavier NX ideal for large-scale smart city, retail, and security monitoring deployments.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDrone \u0026amp; UAV Payloads\u003c\/strong\u003e — The 10W low-power mode and compact 69.6 × 45 mm footprint enable integration into weight-critical aerial platforms for real-time aerial reconnaissance, infrastructure inspection, and autonomous mapping with fully onboard AI processing.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAIoT Gateway \u0026amp; Edge Inference Nodes\u003c\/strong\u003e — Cloud-native container support and GbE networking make the Xavier NX an efficient AIoT hub — running inference locally, aggregating sensor data, and selectively pushing processed results to cloud dashboards with minimal bandwidth consumption.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAI Research \u0026amp; Prototyping\u003c\/strong\u003e — Universities and R\u0026amp;D teams use the Xavier NX as a production-representative edge AI platform — its full CUDA, TensorRT, and JetPack ecosystem mirrors the toolchain used in large-scale deployments, making benchmarks directly transferable to product.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 10px;color:#e0e0e0;\"\u003eWhat's in the Box\u003c\/h3\u003e\n\u003cul style=\"margin:0 0 12px;padding-left:22px;line-height:1.8;color:#e0e0e0;\"\u003e\n  \u003cli\u003e1× NVIDIA Jetson Xavier NX Module (SoM) — your selected configuration (8GB or 16GB)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp style=\"font-size:13px;margin:0 0 20px;line-height:1.6;color:#a0a0a0;\"\u003e\u003cem\u003eNote: a carrier board, heatsink, thermal pad, power supply, and any cameras, display adapters, or expansion cards are not included with the module. These accessories are sold separately. A compatible carrier board and appropriate power supply are required before the module can be used.\u003c\/em\u003e\u003c\/p\u003e\n\n\u003ch3 style=\"font-size:1.15em;font-weight:700;margin:24px 0 16px;color:#e0e0e0;\"\u003eFrequently Asked Questions\u003c\/h3\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat carrier boards are compatible with the Jetson Xavier NX Module?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Xavier NX uses a standard \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e260-pin SO-DIMM connector\u003c\/span\u003e and is compatible with any carrier board explicitly designed for the Xavier NX family — including the official NVIDIA Jetson Xavier NX Developer Kit carrier and third-party boards from Connect Tech, Auvidea, Seeed Studio, and Forecr. Critically, the Xavier NX is \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003enot electrically compatible with the original Jetson Nano carrier board\u003c\/span\u003e despite sharing the same physical SO-DIMM form factor — the two platforms use different pin assignments and cannot be interchanged. Always confirm that the carrier board vendor explicitly lists Jetson Xavier NX support before purchasing. Third-party carriers often add ruggedised connectors, multiple GbE ports, M.2 NVMe slots, and extended temperature ratings not found on the reference design.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat power supply does the Jetson Xavier NX require?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003ePower is delivered through the carrier board, so the exact supply specification is carrier-dependent. The official NVIDIA Xavier NX Developer Kit carrier uses a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e9V–20V DC barrel jack\u003c\/span\u003e (centre-positive, 5.5mm\/2.5mm), with a 19V adapter typically recommended to cover the module plus attached peripherals. The module supports three configurable power modes — \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e10W, 15W, and 20W\u003c\/span\u003e — and your supply must sustain the peak wattage of your chosen mode plus headroom for cameras, SSDs, and USB devices. For the 20W mode under full AI inference load, a supply rated at 40W or more is advisable to avoid voltage droop and instability. Always consult your specific carrier board's datasheet for exact input voltage range and connector requirements.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhich operating system and JetPack version should I use?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Xavier NX officially supports \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA JetPack 4.x and JetPack 5.x\u003c\/span\u003e, with JetPack 5.1.x being the current long-term supported release for the Xavier series, based on \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUbuntu 20.04 LTS\u003c\/span\u003e. JetPack 5 ships with CUDA 11.4, TensorRT 8.x, cuDNN 8.x, OpenCV, VPI, and the Multimedia API pre-installed and ready to use. Note that \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetPack 6 is not supported on Xavier NX\u003c\/span\u003e — it requires the Orin-generation SoC. NVIDIA's SDK Manager tool on a Linux Ubuntu host handles flashing, firmware updates, and SDK package installation in a guided workflow. For new projects, JetPack 5.1.x is the recommended baseline.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat storage options are available — does it need an SSD to boot?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eUnlike the Jetson Orin Nano, the Jetson Xavier NX includes \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e16GB of onboard eMMC 5.1 storage\u003c\/span\u003e — the module can boot directly from internal flash without any external SSD or microSD card. This simplifies initial setup and reduces BOM cost for production designs. For applications requiring more capacity or faster I\/O, most carrier boards expose an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eM.2 M-key NVMe SSD slot via PCIe Gen3\u003c\/span\u003e — this is preferred for large model storage, datasets, and logging. A 128GB or 256GB NVMe SSD is a common production pairing. The onboard eMMC can simultaneously host the OS while the NVMe SSD is used exclusively for data and AI model storage.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat additional hardware do I need to get started?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eAt minimum you need: a compatible \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eXavier NX carrier board\u003c\/span\u003e, a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eheatsink with thermal interface material\u003c\/span\u003e (active cooling recommended for sustained 15W\/20W loads), and a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003epower supply\u003c\/span\u003e matched to your carrier. Because the Xavier NX has onboard eMMC, you do not need to purchase a boot SSD for initial bring-up — though additional storage is recommended for production workloads. For first-time setup, a display (HDMI or DP adapter), USB keyboard, and mouse are useful — though headless SSH setup is also viable after the first flash. A host PC running \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUbuntu 20.04 or 22.04 with NVIDIA SDK Manager\u003c\/span\u003e is required to flash JetPack onto the module for the first time.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eHow does the Jetson Xavier NX compare to the Jetson Orin NX?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Orin NX\u003c\/span\u003e is the next-generation successor and delivers a significant performance step up — up to 100 TOPS (Orin NX 16GB) versus 21 TOPS on the Xavier NX. The Orin NX uses the newer \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eAmpere GPU architecture\u003c\/span\u003e (vs. Volta on Xavier NX), supports JetPack 6 on Ubuntu 22.04, and offers higher memory bandwidth via LPDDR5. However, the Jetson Xavier NX remains a strong production-proven platform with a mature software ecosystem, a large installed carrier board base, and critically — \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ehardware video encoding (NVENC)\u003c\/span\u003e which the Jetson Orin Nano lacks entirely. The Xavier NX is the right choice for projects already aligned to JetPack 5 or requiring the specific 21 TOPS Volta performance envelope with full hardware encode\/decode.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eHow many GPIO and serial interface pins are available?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Xavier NX exposes a comprehensive I\/O set through its 260-pin SO-DIMM connector, including \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUART, SPI, I2C, I2S, CAN bus interfaces, GPIO, and PWM outputs\u003c\/span\u003e. The exact signals accessible to your application depend on your carrier board design — the official NVIDIA Developer Kit routes a subset to a 40-pin expansion header with a layout broadly familiar to Raspberry Pi users. The CAN bus interface is particularly valuable for robotics and automotive applications requiring deterministic communication with motor controllers and sensor arrays. For custom carrier board designs, NVIDIA's Xavier NX pinmux spreadsheet fully documents every signal assignment and alternate function available on the SO-DIMM connector.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eIs this module suitable for beginners, or is it aimed at professionals?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe standalone Jetson Xavier NX Module (SoM) is primarily aimed at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eembedded engineers, OEM product designers, and experienced AI developers\u003c\/span\u003e building custom hardware around the module. If you are new to Jetson and want to experiment first, the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Xavier NX Developer Kit\u003c\/span\u003e — which bundles a carrier board — is the recommended starting point. Once comfortable with JetPack, TensorRT model deployment, and system bring-up workflows, the standalone module is the correct choice for custom carrier board designs and production scaling. NVIDIA's \"Hello AI World\" tutorial series and the active Jetson Developer Forum community substantially lower the learning curve regardless of prior Jetson experience.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat is the most common mistake users make when setting up the Xavier NX?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe most frequent mistake is attempting to use a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Nano carrier board\u003c\/span\u003e with the Xavier NX module — despite the same physical SO-DIMM form factor, the two platforms are electrically incompatible and the module will not function correctly on a Nano carrier. A second common error is selecting a power supply sized only for the module's nominal wattage without accounting for attached peripherals — under-powered supplies cause random reboots and instability under AI inference load. Finally, many users overlook that \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003einitial JetPack flashing requires a Linux Ubuntu host PC\u003c\/span\u003e running NVIDIA SDK Manager — this step cannot be performed from a Windows machine directly, so plan your bring-up environment before the hardware arrives.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 4px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhere can I find official documentation, firmware updates, and community support?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eAll official documentation, hardware design guides, datasheets, pinmux tools, and JetPack firmware are hosted on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Jetson Developer Zone\u003c\/span\u003e at developer.nvidia.com\/embedded. NVIDIA's \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eSDK Manager\u003c\/span\u003e tool manages JetPack flashing and software updates from a Ubuntu host machine. Community support, model optimisation discussions, hardware bring-up guidance, and carrier board recommendations are available on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer Forums — Jetson \u0026amp; Embedded Systems\u003c\/span\u003e section, actively monitored by NVIDIA engineers and the wider Jetson community. For AI deployment tutorials, NVIDIA's \"Hello AI World\" and Jetson AI Fundamentals courses are specifically designed around the Jetson Xavier hardware platform and are the recommended starting point for new users.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"8GB","offer_id":43036785901673,"sku":"NVD-005","price":62999.99,"currency_code":"INR","in_stock":true},{"title":"16GB","offer_id":43036785934441,"sku":"NVD-006","price":82149.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/Image1_720x_b3e99765-4eab-405b-bc1c-821b1e1b868c.webp?v=1774263461","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-xavier-nx-module","provider":"EdgeTech Robotics","version":"1.0","type":"link"}