{"product_id":"nvidia-jetson-orin-nx-module","title":"NVIDIA Jetson Orin NX Module","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;color:#e0e0e0;\"\u003eNVIDIA Jetson Orin NX 8GB — 70 TOPS Edge AI — 1024-Core Ampere GPU — Compact SO-DIMM Module\u003c\/h2\u003e\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe \u003cstrong\u003eNVIDIA Jetson Orin NX 8GB\u003c\/strong\u003e delivers up to \u003cstrong\u003e70 TOPS\u003c\/strong\u003e of INT8 AI performance — scaling to 117 TOPS in MAXN_SUPER mode with JetPack 6.2 — within the ultra-compact 69.6×45mm SO-DIMM form factor. It combines a 1024-core NVIDIA Ampere GPU, 6-core Arm Cortex-A78AE CPU, dedicated NVDLA v2.0 deep learning acceleration, and hardware video encode\/decode into a configurable 10W–40W thermal envelope, making it the go-to compute module for drones, autonomous robots, smart cameras, and portable industrial AI systems. Available alongside the Orin NX 16GB for deployments demanding greater memory capacity and additional CPU cores.\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\u003e70 TOPS AI Performance — 117 TOPS in MAXN_SUPER Mode\u003c\/strong\u003e — delivers real-time deep learning inference for vision, NLP, and multi-sensor fusion pipelines; JetPack 6.2 unlocks MAXN_SUPER for a 67% throughput boost without a hardware change.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e1024-Core NVIDIA Ampere GPU with 32 Tensor Cores\u003c\/strong\u003e — mixed-precision matrix operations (FP32, FP16, INT8, INT4) accelerate neural network layers at sub-millisecond latency, enabling concurrent multi-model inference on a single module.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e1× NVDLA v2.0 Deep Learning Accelerator\u003c\/strong\u003e — offloads steady-state inference from the GPU at up to 20 TOPS, freeing Ampere cores for pre\/post-processing, sensor fusion, and secondary model workloads running in parallel.\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 at 2 GHz\u003c\/strong\u003e — automotive-grade 64-bit cores with hardware ECC handle ROS2 node execution, sensor preprocessing, and Linux OS management without thermal throttling under sustained workloads.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e8GB 128-Bit LPDDR5 at 102.4 GB\/s\u003c\/strong\u003e — wide memory bandwidth prevents bottlenecks when streaming multiple high-resolution camera feeds or running concurrent multi-model inference — double the bandwidth of the previous Xavier NX generation.\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 — a dedicated VPU handles 4K60 H.265 encoding and 8K30 H.265 decoding entirely in hardware, preserving CPU and GPU resources for AI inference tasks running simultaneously.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eUp to 4 MIPI CSI-2 Cameras (8 via Virtual Channels)\u003c\/strong\u003e — 8 D-PHY 1.2 lanes at 20 Gbps aggregate support stereo depth rigs, 360° vision arrays, and simultaneous RGB\/thermal capture without an external frame grabber.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003ePCIe Gen 4 Connectivity (1×4 + 3×1 lanes)\u003c\/strong\u003e — supports high-throughput NVMe SSDs, FPGA accelerators, and multi-port networking cards directly on the module bus, with substantially lower latency than USB-attached peripherals.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eConfigurable TDP: 10W to 40W\u003c\/strong\u003e — four selectable power modes let you tune performance against battery life or thermal budget at deployment time, with no firmware reflash required when switching between modes.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eXavier NX Carrier Board Compatible\u003c\/strong\u003e — the 260-pin SO-DIMM interface is pin-compatible with Jetson Xavier NX carrier boards, minimising redesign effort and bill-of-materials changes when migrating existing platforms to Orin-class performance.\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;\"\u003eDetails\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;\"\u003e70 TOPS (INT8) — 117 TOPS (MAXN_SUPER, JetPack 6.2+)\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;\"\u003eNVIDIA Ampere Architecture — 1024 CUDA Cores, 32 Tensor Cores, 1173 MHz max\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;\"\u003e6-Core Arm® Cortex®-A78AE v8.2 64-bit — 2 GHz max — 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;\"\u003e8GB 128-bit LPDDR5 — 102.4 GB\/s bandwidth\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;\"\u003eNo on-module storage — external NVMe SSD via carrier board M.2 slot\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;\"\u003e1× NVDLA v2.0 (up to 20 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;\"\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;\"\u003e1× PVA v2.0\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;\"\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;\"\u003e1×4K60 | 3×4K30 | 6×1080p60 | 12×1080p30 (H.265) — H.264, AV1\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;\"\u003e1×8K30 | 2×4K60 | 4×4K30 | 9×1080p60 | 18×1080p30 (H.265) — H.264, VP9, AV1\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;\"\u003eUp to 4 cameras (8 via virtual channels) — 8 MIPI CSI-2 lanes — D-PHY 1.2 (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;\"\u003eDisplay\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1× 8K30 multi-mode DP 1.4a (+MST) \/ eDP 1.4a \/ HDMI 2.1\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;\"\u003e1×4 + 3×1 (PCIe Gen 4, 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;\"\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;\"\u003e1× Gigabit Ethernet\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;\"\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;\"\u003e10W \/ 15W \/ 25W \/ 40W (MAXN_SUPER, JetPack 6.2+)\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;\"\u003e69.6mm × 45mm — 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 NX Is Right for You?\u003c\/h3\u003e\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eBoth the 8GB and 16GB share the same Ampere GPU, PCIe Gen 4 connectivity, and SO-DIMM form factor — the key differentiators are memory capacity, CPU core count, and peak AI throughput. Choose the 8GB for compact single-model deployments and power-sensitive platforms; choose the 16GB when running concurrent models, higher-resolution pipelines, or multi-agent robotic stacks that demand larger working memory.\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 NX 8GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eOrin 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;\"\u003e70 TOPS (117 TOPS MAXN_SUPER)\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e100 TOPS (157 TOPS MAXN_SUPER)\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;\"\u003e1024 CUDA Cores, 32 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;\"\u003eCPU\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e6-Core A78AE, 2 GHz\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e8-Core A78AE, 2 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;\"\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 LPDDR5 — 102.4 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 LPDDR5 — 102.4 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;\"\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;\"\u003e1× NVDLA v2.0\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2× NVDLA v2.0\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;\"\u003ePower Modes\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e10W \/ 15W \/ 25W \/ 40W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e10W \/ 15W \/ 25W \/ 40W\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;\"\u003eDrones, handheld systems, single-model inference, battery-powered platforms\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMulti-model pipelines, robotic arms, high-res video AI, concurrent sensor fusion stacks\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\u003eAutonomous Drones \u0026amp; UAVs\u003c\/strong\u003e — the 10W–25W power envelope and compact SO-DIMM form factor fit directly into UAV flight computers, enabling onboard obstacle avoidance, target tracking, and real-time path planning without a tethered compute unit.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eIndustrial Robot Vision\u003c\/strong\u003e — runs real-time object detection, grasp pose estimation, and defect classification simultaneously, with hardware CSI-2 camera support for synchronised multi-camera bin-picking and assembly verification rigs.\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-bandwidth LPDDR5 and hardware video decode handle line-scan and area-scan camera feeds at production-line speeds for PCB, semiconductor, and pharmaceutical surface inspection.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eCollaborative Robots (Cobots)\u003c\/strong\u003e — runs ROS2 navigation stacks, sensor fusion middleware, and safety watchdog processes concurrently on dedicated CPU cores while the GPU handles visual odometry and real-time scene understanding.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart Traffic \u0026amp; Surveillance Systems\u003c\/strong\u003e — hardware H.265 encode\/decode and DeepStream SDK support multi-stream vehicle tracking, licence plate recognition, and crowd analytics entirely at the edge without cloud connectivity or data egress costs.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHandheld Medical Imaging Devices\u003c\/strong\u003e — configurable TDP and compact form factor enable battery-powered ultrasound, retinal scanning, and dermatology AI tools that require real-time inference without transmitting patient data to the cloud.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eEdge Inference Servers\u003c\/strong\u003e — TensorRT-optimised INT8 models with NVDLA offloading make the Orin NX 8GB a capable low-power inference server for smart factory edge nodes, retail AI kiosks, and digital signage analytics platforms.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eNatural Language Processing at the Edge\u003c\/strong\u003e — 8GB LPDDR5 is sufficient to run quantised large language models and speech recognition pipelines for voice-controlled industrial HMIs and autonomous service robot interfaces.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAutonomous Ground Vehicles (AGVs)\u003c\/strong\u003e — paired with a carrier board providing CAN bus, UART, and GPIO breakouts, the Orin NX 8GB integrates directly into AGV motor controllers, LiDAR processing pipelines, and fleet management stacks.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eResearch \u0026amp; Academic Prototyping\u003c\/strong\u003e — the SO-DIMM carrier board ecosystem, JetPack SDK, and NVIDIA NGC model catalogue provide a well-documented, production-representative platform for robotics labs and universities developing next-generation embodied AI systems.\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 NX 8GB Module (with integrated Thermal Transfer Plate)\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, NVMe SSD, cables, cameras, and display adapters are sold separately and not included with the module. 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 NX 8GB?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson Orin NX 8GB uses a standard \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e260-pin SO-DIMM connector\u003c\/span\u003e that is pin-compatible with carrier boards designed for the Jetson Xavier NX family, allowing most existing carrier board designs to be reused with a JetPack firmware update. Third-party carriers from Connect Tech, Seeed Studio, Auvidea, and Forecr also explicitly support the Orin NX module. Always verify the carrier board vendor lists Orin NX compatibility, since the older Jetson Nano carrier boards use an incompatible connector and are not interchangeable. A carrier board with an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eM.2 Key M slot\u003c\/span\u003e is strongly recommended, as it is required for NVMe SSD storage which serves as the primary boot and OS medium.\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 NX 8GB require?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003ePower is delivered entirely through the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e260-pin SO-DIMM carrier board connector\u003c\/span\u003e — there is no separate power input on the module. The carrier board regulates the supply; most commercial designs accept \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e9V–20V DC input\u003c\/span\u003e, with a 19V \/ 65W adapter commonly used to cover all peripherals. Total system draw in standard modes ranges from 10W to 25W; enabling \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eMAXN_SUPER mode\u003c\/span\u003e with JetPack 6.2 can push peak draw to 40W. For battery-powered designs, budget at least 20W headroom above the chosen TDP to handle transient GPU and CPU burst peaks.\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 software frameworks are supported?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Orin NX 8GB runs Ubuntu-based Linux via the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA JetPack SDK\u003c\/span\u003e, which bundles CUDA, cuDNN, TensorRT, DeepStream, and VPI in a tested, unified software stack. Both JetPack 5.x (Ubuntu 20.04) and \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetPack 6.x (Ubuntu 22.04 LTS)\u003c\/span\u003e are supported, with JetPack 6.2 adding MAXN_SUPER performance mode. \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ePyTorch, TensorFlow, and ONNX Runtime\u003c\/span\u003e are available via JetPack-aligned wheel packages, and ROS2 Humble and Jazzy integrate natively. Docker containers are supported for isolated pipeline deployments, and NVIDIA's NGC catalogue provides hundreds of pre-optimised models ready for TensorRT deployment.\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 NX 8GB have built-in storage, and what options are available?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Orin NX 8GB has \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eno on-module flash or eMMC\u003c\/span\u003e — all OS and application storage is provided externally through the carrier board. The recommended option is an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSD via M.2 Key M (PCIe Gen 4)\u003c\/span\u003e, which delivers the best sustained read\/write performance for AI workloads, logging-heavy pipelines, and dataset storage. Some carrier boards expose a microSD slot as an alternative, but SD card I\/O will bottleneck demanding workloads. A capacity of at least 64GB is recommended to accommodate JetPack, AI models, application code, and log files 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 accessories are required to get started with the Jetson Orin NX 8GB?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eAt minimum you need a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ecompatible carrier board\u003c\/span\u003e, an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSD\u003c\/span\u003e for storage, and a DC power supply matched to the carrier's input specification. A display cable (HDMI 2.1 or DisplayPort 1.4a), USB keyboard, and mouse are recommended for initial setup, along with a host PC running \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e on Ubuntu to flash JetPack. A \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ethermal solution\u003c\/span\u003e — heatsink with thermal interface material and, ideally, an active cooling fan for sustained loads above 15W — is essential; the module includes a Thermal Transfer Plate but requires external cooling hardware to remain within thermal limits.\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 NX 8GB compare to the Jetson Xavier NX?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Orin NX 8GB delivers approximately \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e5× the AI throughput\u003c\/span\u003e of the Xavier NX 8GB — from roughly 21 TOPS to 70 TOPS (117 TOPS MAXN_SUPER) — while maintaining the same SO-DIMM form factor for drop-in carrier board reuse. The \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eAmpere GPU\u003c\/span\u003e replaces the older Volta architecture, adding INT4 precision and significantly improved Tensor Core efficiency per watt. Memory bandwidth doubles from 51.2 GB\/s to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e102.4 GB\/s\u003c\/span\u003e thanks to LPDDR5, and PCIe upgrades from Gen 3 to Gen 4, delivering higher peripheral throughput. Power modes are also more granular on the Orin NX, offering 10W, 15W, 25W, and 40W profiles versus the Xavier NX's 10W and 15W options.\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 GPIO and serial interfaces are available on the Jetson Orin NX 8GB?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThrough a compatible carrier board, the Orin NX 8GB exposes \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e3× UART, 2× SPI, 2× I2S audio, 4× I2C, 1× CAN bus\u003c\/span\u003e, DMIC and DSPK digital audio, PWM outputs, and multiple configurable GPIO lines. PCIe provides \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e1×4 + 3×1 Gen 4 lanes\u003c\/span\u003e, while USB includes 3× USB 3.2 Gen2 (10 Gbps) and 3× USB 2.0 host ports. The CAN bus interface is particularly valuable for robotics and AGV applications requiring deterministic real-time communication with motor controllers and sensors. The exact signals available depend on your carrier board design — consult the carrier schematic to confirm interface routing before connecting external hardware.\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 the Jetson Orin NX 8GB 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 Orin NX 8GB SoM is primarily aimed at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eintermediate-to-advanced developers\u003c\/span\u003e — embedded engineers, AI developers, and OEM product teams building custom hardware. Initial setup requires flashing JetPack via SDK Manager, configuring carrier board device trees, and managing Linux networking and storage, all of which assume embedded Linux familiarity. Developers new to Jetson should start with the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Orin Nano Developer Kit\u003c\/span\u003e, which ships as a complete kit. Once your environment is established, the Orin NX integrates smoothly with high-level frameworks like \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ePyTorch, DeepStream, and ROS2\u003c\/span\u003e, significantly lowering the barrier for AI model deployment and iteration.\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 common mistakes should I avoid when deploying the Jetson Orin NX 8GB?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe most common mistake is running at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eMAXN or MAXN_SUPER power mode\u003c\/span\u003e without a properly rated thermal solution — the Orin SoC aggressively throttles CPU and GPU clocks when the die exceeds thermal limits, causing unpredictable latency spikes in production that are difficult to diagnose. Always validate thermals under sustained full-load conditions before finalising an enclosure design. A second frequent issue is \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eattempting to flash without a recognised storage device\u003c\/span\u003e — the module requires a formatted NVMe SSD or compatible storage present on the carrier before SDK Manager can deploy JetPack. Finally, verify your carrier board's \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetPack compatibility list\u003c\/span\u003e before updating firmware, as some board support packages lag behind the latest JetPack release by several weeks.\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, and community support?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eOfficial documentation — including the Orin NX Series datasheet, hardware design guide, and JetPack release notes — is hosted on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Jetson Developer Zone\u003c\/span\u003e at developer.nvidia.com\/embedded. Firmware and the NVIDIA SDK Manager installer are available at developer.nvidia.com\/nvidia-sdk-manager. Community support, carrier board integration guides, and TensorRT optimisation discussions are active on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer Forums — Jetson \u0026amp; Embedded Systems\u003c\/span\u003e section, monitored by NVIDIA engineers. Pre-optimised AI models ready for Jetson deployment — covering object detection, segmentation, pose estimation, and NLP — are available through the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA NGC catalogue\u003c\/span\u003e at ngc.nvidia.com.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"8GB","offer_id":43036766208105,"sku":"NVD-007","price":59599.99,"currency_code":"INR","in_stock":true},{"title":"16GB","offer_id":43036766240873,"sku":"NVD-008","price":90049.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/2-2.jpg?v=1774261891","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-orin-nx-module","provider":"EdgeTech Robotics","version":"1.0","type":"link"}