{"product_id":"nvidia-jetson-orin-nano-module","title":"NVIDIA Jetson Orin Nano Module","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;color:#e0e0e0;\"\u003eNVIDIA Jetson Orin Nano Module — Up to 40 TOPS Edge AI — 1024-Core Ampere GPU — CUDA \u0026amp; JetPack 6 Ready\u003c\/h2\u003e\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe \u003cstrong\u003eNVIDIA Jetson Orin Nano Module\u003c\/strong\u003e is a compact AI System-on-Module (SoM) engineered for real-time edge inferencing across robotics, industrial vision, autonomous machines, and AIoT. Available in 4GB (20 TOPS) and 8GB (40 TOPS) configurations, it connects to any Jetson Orin Nano-compatible carrier board via its standard \u003cstrong\u003e260-pin SO-DIMM interface\u003c\/strong\u003e, giving developers, research labs, and OEMs a scalable, power-efficient path from prototype to production deployment.\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\u003eUp to 40 TOPS AI Throughput\u003c\/strong\u003e — The 8GB variant delivers 40 TOPS of sparse INT8 inference, enabling real-time object detection, segmentation, and pose estimation entirely at the edge — no cloud round-trip or connectivity dependency required.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eNVIDIA Ampere GPU Architecture\u003c\/strong\u003e — 1024 CUDA cores and 32 Tensor Cores on the 8GB (512 cores \/ 16 Tensor Cores on the 4GB) provide hardware-accelerated parallel compute purpose-built for deep learning vision and inference workloads.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHigh-Bandwidth LPDDR5 Memory\u003c\/strong\u003e — 8GB of 128-bit LPDDR5 at 68 GB\/s sustains simultaneous multi-model and multi-stream AI pipelines; the 4GB variant delivers 64-bit LPDDR5 at 34 GB\/s for lighter, single-model workloads.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e6-Core Arm Cortex-A78AE CPU\u003c\/strong\u003e — A high-reliability 64-bit v8.2 CPU running at up to 1.5 GHz handles Linux OS management, sensor preprocessing, and peripheral control in parallel with GPU inference — without performance contention.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMulti-Camera Input Pipeline\u003c\/strong\u003e — Up to 4 simultaneous cameras (expandable to 8 via virtual channels) over 8-lane MIPI CSI-2 with D-PHY 2.1 at up to 20 Gbps aggregate bandwidth — essential for surround-view robotics and multi-sensor fusion systems.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003ePCIe Gen3 High-Speed Expansion\u003c\/strong\u003e — One x4 plus three x1 PCIe Gen3 lanes allow direct attachment of NVMe SSDs, Wi-Fi 6 adapters, AI accelerator cards, and other high-bandwidth peripherals without USB overhead or bottleneck.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eFull CUDA, TensorRT \u0026amp; JetPack Ecosystem\u003c\/strong\u003e — Native support for CUDA, TensorRT, PyTorch, TensorFlow, and NVIDIA JetPack SDK means existing AI models deploy with minimal porting effort and maximum hardware utilisation out of the box.\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 7W, 15W, and 25W power modes let you tune performance vs. thermal budget — critical for battery-powered field devices, passively cooled enclosures, or full-performance embedded applications.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHardware-Accelerated Video Decode\u003c\/strong\u003e — A dedicated NVDEC engine handles up to 1× 4K60 or 11× 1080p30 H.265 streams concurrently, freeing CPU and GPU resources entirely for AI inference tasks running in parallel.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eEcosystem-Wide SO-DIMM Compatibility\u003c\/strong\u003e — The 69.6 × 45 mm module with a 260-pin SO-DIMM connector is pin-compatible with a wide range of third-party carrier boards from Connect Tech, Seeed Studio, Auvidea, and others — accelerating custom system integration and time-to-market.\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;\"\u003eOrin Nano 4GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eOrin Nano 8GB\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;\"\u003e20 TOPS\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e40 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;\"\u003e512-core NVIDIA Ampere, 16 Tensor Cores\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1024-core NVIDIA Ampere, 32 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;\"\u003eMax GPU Frequency\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1.0 GHz\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1.1 GHz\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 Arm Cortex-A78AE v8.2 64-bit, up to 1.5 GHz — 1.5MB 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;\"\u003e4GB 64-bit LPDDR5 — 34 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;\"\u003e8GB 128-bit LPDDR5 — 68 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\"\u003eExternal NVMe SSD via M.2 M-key (PCIe Gen3) — no internal eMMC\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;\"\u003eCamera\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 4 cameras (8 via virtual channels) — 8-lane MIPI CSI-2, D-PHY 2.1 up to 20 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\"\u003e1080p30 software-based (CPU\/FFmpeg) — no dedicated NVENC hardware encoder\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\"\u003e1× 4K60 (H.265) — 2× 4K30 — 5× 1080p60 — 11× 1080p30 (hardware NVDEC)\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\"\u003e1×4 + 3×1 PCIe Gen3 (Root Port \u0026amp; Endpoint)\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;\"\u003eUSB\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\"\u003e3× USB 3.2 Gen2 (10 Gbps) + 3× USB 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\"\u003e1× GbE (10\/100\/1000 Base-T)\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\"\u003e1× 4K30 multi-mode DP 1.2 (+MST) \/ eDP 1.4 \/ HDMI 1.4\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;\"\u003eOther I\/O\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\"\u003e3× UART, 2× SPI, 2× I2S, 4× I2C, 1× CAN, DMIC \u0026amp; DSPK, PWM, GPIOs\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;\"\u003e7W – 10W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e7W – 15W\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 Orin Nano Is Right for You?\u003c\/h3\u003e\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eBoth variants share the same CPU, I\/O set, and connectivity — the decision is purely about memory bandwidth and AI throughput headroom. The 4GB module is the right fit for single-model inference, lightweight NLP tasks, or memory-constrained embedded designs where the lower power ceiling is an asset. The 8GB module is the clear choice whenever you need to run multiple models simultaneously, process 4K multi-stream video analytics, or push sustained throughput above 20 TOPS in production.\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;\"\u003eOrin Nano 4GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eOrin Nano 8GB\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;\"\u003e4GB 64-bit LPDDR5\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 LPDDR5\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;\"\u003e34 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;\"\u003e68 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;\"\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;\"\u003e20 TOPS\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e40 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 Cores\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e512 CUDA cores, 16 Tensor Cores\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1024 CUDA cores, 32 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;\"\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;\"\u003e7W – 10W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e7W – 15W\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-model inference, power-constrained designs, lightweight NLP \u0026amp; vision tasks\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMulti-model pipelines, 4K multi-stream analytics, demanding real-time inference\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\u003eEdge AI Inference\u003c\/strong\u003e — Run YOLO, ResNet, EfficientDet, and transformer-based models entirely on-device, eliminating cloud latency and data privacy risks in real-time decision systems deployed in the field.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eRobotics \u0026amp; AMR \/ AGV Navigation\u003c\/strong\u003e — Multi-camera input and low-latency Ampere GPU inference make the Orin Nano the ideal compute brain for autonomous mobile robots, warehouse AGVs, and collaborative robot arms requiring real-time environmental perception.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eIndustrial Computer Vision \u0026amp; Defect Inspection\u003c\/strong\u003e — Detect surface defects, measure component tolerances, and classify assembly errors on high-speed production lines with sub-millisecond TensorRT-optimised inference — no cloud connectivity required.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAI-Powered Video Surveillance\u003c\/strong\u003e — Process up to 4 simultaneous camera feeds onboard with person detection, anomaly recognition, and license plate identification — without transmitting raw footage off-site or compromising data privacy.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eEdge NLP \u0026amp; Conversational AI\u003c\/strong\u003e — Deploy compact language models and voice-processing pipelines locally on service kiosks, retail assistants, and point-of-care devices where cloud connectivity is unreliable, costly, or prohibited.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMedical Imaging \u0026amp; Point-of-Care Diagnostics\u003c\/strong\u003e — Run inference on X-ray, ultrasound, and digital pathology images at the bedside or in remote clinics using a certified hardware-grade module with defined, repeatable power envelopes.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eRetail Analytics \u0026amp; Loss Prevention\u003c\/strong\u003e — Count customers, monitor shelf occupancy, detect unusual behaviours, and generate heatmaps in real time — all processed on-device to comply with GDPR and regional data protection regulations.\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 7W minimum power mode and compact SoM footprint allow integration into weight-critical UAV payloads for real-time aerial AI, infrastructure inspection, and autonomous mapping missions.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart Agriculture \u0026amp; Precision Farming\u003c\/strong\u003e — Detect plant disease, count crops, and guide autonomous sprayer systems using vision models running on solar or battery-powered field units with no internet dependency.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAI Research \u0026amp; Education\u003c\/strong\u003e — Universities, research labs, and maker communities use the Jetson Orin Nano as an accessible yet production-representative platform for developing, benchmarking, and publishing edge AI architectures.\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 Orin Nano Module (SoM) — your selected configuration (4GB or 8GB)\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, cables, NVMe SSD, and any cameras or display adapters 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 Orin Nano Module?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Orin Nano Module 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 designed for the Jetson Orin Nano or Jetson Orin NX family. This includes the official \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Jetson Orin Nano Developer Kit\u003c\/span\u003e carrier, plus third-party boards from Connect Tech, Seeed Studio, Auvidea, and Forecr, among others. Always verify that the carrier board vendor explicitly lists support for the Orin Nano module, since the older Jetson Nano carrier boards use an incompatible connector and are not interchangeable. Third-party carriers often add features like multiple Ethernet ports, M.2 expansion, or ruggedised I\/O 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 Orin Nano Module 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 supply specification depends on your carrier design. The official NVIDIA 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), and a 19V \/ 65W adapter is typically recommended to cover all peripherals. The module itself operates across configurable power modes of \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e7W, 15W, and 25W\u003c\/span\u003e — ensure your supply can sustain the peak wattage of your chosen mode plus headroom for attached peripherals such as SSDs, cameras, and USB devices. Third-party carrier boards may specify different input voltage ranges — always consult the carrier datasheet before selecting a power supply.\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 systems and JetPack versions are supported?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Orin Nano Module officially supports \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA JetPack 5.x and JetPack 6.x\u003c\/span\u003e. The current production release is JetPack 6.2, based on \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUbuntu 22.04 LTS\u003c\/span\u003e with Linux Kernel 5.15 — it ships with CUDA, TensorRT, cuDNN, OpenCV, VPI, and Multimedia API pre-installed. JetPack 5.x releases are based on Ubuntu 20.04 and remain supported for projects requiring that baseline. Modules shipped with early firmware may require a firmware update before JetPack 6.x can be flashed — NVIDIA's SDK Manager tool handles this process automatically on a Linux host.\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 — do I need an SD card or eMMC?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eUnlike the original Jetson Nano, the Jetson Orin Nano Module has \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eno internal eMMC or onboard flash\u003c\/span\u003e — external storage is required to boot. The recommended option is an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSD via M.2 M-key (PCIe Gen3)\u003c\/span\u003e, which is the primary boot medium on the official developer kit and delivers the best sustained read\/write performance for AI workloads. Some carrier boards also provide a microSD slot usable for booting or data storage, but NVMe is strongly preferred. Choose an NVMe SSD of at least 32GB; 128GB or larger is recommended to accommodate JetPack, models, datasets, and application logs comfortably.\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 with this module?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eAt minimum you will need: a compatible \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ecarrier board\u003c\/span\u003e, an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSD\u003c\/span\u003e (for the OS and JetPack), a suitable \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003epower supply\u003c\/span\u003e matched to the carrier, and a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eheatsink with thermal interface material\u003c\/span\u003e (active cooling is recommended for sustained loads above 15W). For initial setup, a display (HDMI or DisplayPort adapter), USB keyboard, and mouse are useful — though headless setup over SSH is also possible after the first flash. A host PC running Ubuntu 20.04 or 22.04 with NVIDIA SDK Manager installed 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 Orin Nano compare to the original Jetson Nano?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Orin Nano is a generational leap beyond the original Jetson Nano (2019). The Orin Nano delivers up to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e40× more AI performance\u003c\/span\u003e, moves from a Maxwell GPU to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Ampere architecture\u003c\/span\u003e, and adds LPDDR5 memory with significantly higher bandwidth. It also gains proper JetPack 5\/6 support on Ubuntu 20.04\/22.04, while the original Jetson Nano reached end-of-life at JetPack 4.6. Critically, the two platforms are physically and software-incompatible — carrier boards, images, and GPIO libraries are not interchangeable — so migrating requires a full system redesign rather than a simple module swap.\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 module exposes a rich I\/O set through the 260-pin SO-DIMM connector: \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e3× UART, 2× SPI, 2× I2S, 4× I2C, 1× CAN bus, PWM outputs, and multiple GPIOs\u003c\/span\u003e — plus digital microphone (DMIC) and digital speaker (DSPK) interfaces for audio. The exact signals available to your application depend on your carrier board design; the official 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 real-time communication with motor controllers and sensors.\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 an advanced platform?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe standalone Jetson Orin Nano Module (SoM) is primarily targeted at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eintermediate-to-advanced users\u003c\/span\u003e — embedded engineers, AI developers, and OEM product teams building custom hardware around the SoM. If you are new to Jetson and want to experiment first, the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Orin Nano Developer Kit\u003c\/span\u003e (which bundles a carrier board) is the recommended starting point. Once you are comfortable with JetPack, model deployment workflows, and system integration, the standalone module is the correct choice for designing custom carrier boards or scaling into production. NVIDIA's \"Hello AI World\" and \"Jetson AI Fundamentals\" tutorials, along with the active Jetson Developer Forum community, substantially lower the learning curve.\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;\"\u003eDoes the Jetson Orin Nano support hardware video encoding?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThis is one of the most important things to verify before purchasing: the Jetson Orin Nano \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003edoes not include a dedicated hardware video encoder (NVENC)\u003c\/span\u003e — this is a confirmed hardware limitation versus larger Orin variants like the Orin NX and AGX Orin. Video encoding (H.264\/H.265 output streams) must be handled in software via \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eCPU cores using FFmpeg or GStreamer\u003c\/span\u003e, limiting simultaneous encode to approximately 1080p30. If your application requires concurrent hardware encoding of multiple high-resolution streams, the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Orin NX\u003c\/span\u003e is the appropriate upgrade. Video decode, by contrast, is fully hardware-accelerated via the dedicated NVDEC engine and supports up to 4K60 H.265.\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 files, datasheets, 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 handles JetPack flashing and software updates from a Ubuntu host. Community support, model optimisation discussions, and hardware bring-up guidance are available on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer Forums — Jetson \u0026amp; Embedded Systems\u003c\/span\u003e section, which is actively monitored by NVIDIA engineers. For AI deployment tutorials, the freely available \"Hello AI World\" and \"Jetson AI Fundamentals\" courses are specifically designed around Jetson Orin Nano hardware and are the recommended starting point for new users.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"4GB","offer_id":43114356310121,"sku":"NVD-002","price":28599.99,"currency_code":"INR","in_stock":true},{"title":"8GB","offer_id":43194982760553,"sku":null,"price":31899.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/IMG-0404.jpg?v=1778242970","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-orin-nano-module","provider":"EdgeTech Robotics","version":"1.0","type":"link"}