{"product_id":"nvidia-jetson-agx-orin-developer-kit64gb","title":"NVIDIA JETSON AGX ORIN DEVELOPER KIT(64GB)","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;\"\u003eNVIDIA Jetson AGX Orin Developer Kit — 275 TOPS Edge AI — 64 GB LPDDR5 — 2048-Core Ampere GPU\u003c\/h2\u003e\n\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe \u003cstrong\u003eNVIDIA Jetson AGX Orin Developer Kit\u003c\/strong\u003e is NVIDIA's most powerful edge AI platform, delivering up to \u003cstrong\u003e275 TOPS of AI performance\u003c\/strong\u003e — up to 8× more than Jetson AGX Xavier — from a compact module that shares the same form factor and software stack across the entire Orin family. Powered by a 12-core Arm Cortex-A78AE CPU, 2048-core Ampere GPU, dual NVDLA v2.0 deep-learning accelerators, and 64 GB of LPDDR5 unified memory, it is built for robotics, autonomous machines, industrial vision, and on-device generative AI inference backed by the full JetPack 6 SDK.\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\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\u003e275 TOPS AI Performance\u003c\/strong\u003e — Delivers up to 8× more INT8 throughput than the Jetson AGX Xavier, enabling real-time inference on large transformer, vision, and diffusion models at the edge without cloud dependency.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e2048-Core NVIDIA Ampere GPU with 64 Tensor Cores\u003c\/strong\u003e — Full CUDA 12 support accelerates TensorRT deployment, mixed-precision workloads, and sparse tensor operations in a single unified memory space shared with the CPU.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e64 GB 256-bit LPDDR5 — 204.8 GB\/s Bandwidth\u003c\/strong\u003e — Shared GPU–CPU memory eliminates data-copy overhead, making it practical to run multi-billion-parameter models and large vision pipelines without batching constraints.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDual NVDLA v2.0 + PVA v2.0 Accelerators\u003c\/strong\u003e — Dedicated silicon for DNN inference and computer-vision pre\/post-processing frees GPU cycles for the most latency-sensitive tasks in the pipeline.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMulti-Camera \u0026amp; High-Throughput Video Pipeline\u003c\/strong\u003e — 16-lane MIPI CSI-2 input with hardware encode up to 4K60 and decode up to 8K30 supports multi-stream analytics and spatial AI systems with minimal CPU overhead.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eConfigurable 15–60 W TDP with Four Power Modes\u003c\/strong\u003e — Software-selectable 15 W, 30 W, 50 W, and 60 W MAXN modes let you tune performance vs. power budget without changing hardware — ideal for both lab and deployed environments.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003ePCIe Gen4, 10 GbE, Wi-Fi 5, Bluetooth 5.0 \u0026amp; Rich Expansion I\/O\u003c\/strong\u003e — x16 PCIe Gen4 slot, M.2 Key M (x4 PCIe Gen4 NVMe), M.2 Key E, 10 Gigabit Ethernet, 6× USB 3.2 ports, DisplayPort 1.4a, and a 40-pin GPIO header cover virtually every sensor or peripheral integration scenario.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eFull NVIDIA JetPack 6 SDK Ecosystem\u003c\/strong\u003e — Ships ready to flash with JetPack 6.x (CUDA 12.6, TensorRT 10.3, cuDNN 9.3, VPI 3.2), Isaac ROS, TAO Toolkit, and DeepStream — the same APIs used in NVIDIA data-centre systems, so models move from cloud to edge without rewriting inference code.\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\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;\"\u003eUp to 275 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;\"\u003eNVIDIA Ampere — 2048 CUDA cores, 64 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;\"\u003e12-core Arm Cortex-A78AE v8.2 64-bit @ 2.2 GHz (3 MB L2 + 6 MB 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;\"\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;\"\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;\"\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;\"\u003ePVA 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;\"\u003eMemory\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e64 GB 256-bit LPDDR5 — 204.8 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;\"\u003e64 GB eMMC 5.1 + M.2 Key M (x4 PCIe Gen4) NVMe expansion\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;\"\u003e2× 4K60 | 4× 4K30 | 8× 1080p60 | 16× 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;\"\u003e1× 8K30 | 3× 4K60 | 6× 4K30 | 12× 1080p60 | 24× 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;\"\u003eCamera\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e16-lane MIPI CSI-2 connector\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;\"\u003e10 GbE (RJ45) + Wi-Fi 802.11ac + Bluetooth 5.0 (pre-installed M.2 module)\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;\"\u003ex16 slot (x8 PCIe Gen4) + M.2 Key M (x4 PCIe Gen4) + M.2 Key E (x1 PCIe Gen4)\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;\"\u003e2× USB 3.2 Gen2 Type-C | 2× USB 3.2 Gen2 Type-A | 2× USB 3.2 Gen1 Type-A | 1× USB 2.0 Micro-B\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;\"\u003eDisplayPort 1.4a with MST support\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;\"\u003eGPIO \/ Expansion\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e40-pin header (I2C, GPIO, SPI, CAN, I2S, UART, DMIC), 12-pin automation header, 10-pin audio header, 10-pin JTAG, 4-pin fan, 2-pin RTC\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;\"\u003e15 W \/ 30 W \/ 50 W \/ 60 W MAXN (software-configurable), DC barrel jack 7–20 V\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;\"\u003eDimensions\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e110 mm × 110 mm × 71.65 mm\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 Is Right for You?\u003c\/h3\u003e\n\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eThe Jetson Orin family scales from 67 TOPS up to 275 TOPS — all sharing the same Ampere GPU architecture, JetPack SDK, and software pipeline. Choose your module based on AI workload size, memory footprint, and power budget; the same Isaac ROS and TensorRT code runs on every variant without modification.\u003c\/p\u003e\n\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:600px;border:0;\"\u003e\n        \u003ctr\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eModel\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAI Performance\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eGPU Cores\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eCPU Cores\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eMemory\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003ePower\u003c\/td\u003e\n        \u003c\/tr\u003e\n        \u003ctr\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:700;word-wrap:break-word;color:#BAFF02;\"\u003eJetson AGX Orin Dev Kit\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:700;word-wrap:break-word;color:#BAFF02;\"\u003e275 TOPS\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2048 Ampere\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e12-core A78AE\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e64 GB LPDDR5\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e15–60 W\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;\"\u003eJetson AGX Orin 32GB\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e200 TOPS\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2048 Ampere\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\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e32 GB LPDDR5\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e15–40 W\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;\"\u003eJetson Orin NX 16GB\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e157 TOPS\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1792 Ampere\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\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e16 GB LPDDR5\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e10–40 W\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;\"\u003eJetson Orin Nano 8GB\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e67 TOPS\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1024 Ampere\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e6-core A78AE\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e8 GB LPDDR5\u003c\/td\u003e\n            \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e7–25 W\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\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 Mobile Robots (AMR)\u003c\/strong\u003e — Run simultaneous SLAM, multi-sensor fusion, path planning, and obstacle avoidance in real time using all compute engines on a single board, replacing entire racks of compute in earlier robot architectures.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eIndustrial Machine Vision \u0026amp; Quality Control\u003c\/strong\u003e — Process up to 16 simultaneous 1080p camera feeds with hardware decode and run defect-detection CNNs via NVDLA at throughputs that match high-speed production-line rates.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart City \u0026amp; Traffic Analytics\u003c\/strong\u003e — Deploy multi-stream 4K video analytics for vehicle counting, licence-plate recognition, and anomaly detection at the edge without transmitting raw video to the cloud.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMedical Imaging AI\u003c\/strong\u003e — Perform on-device inference for CT, MRI, and ultrasound analysis with the large unified memory pool enabling full-resolution 3D model inference that GPU-constrained devices cannot fit in memory.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAutonomous Drones \u0026amp; UAVs\u003c\/strong\u003e — Execute low-latency real-time perception, obstacle avoidance, and landing-zone detection without cloud round-trips, with configurable TDP keeping airborne power budgets manageable.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eGenerative AI \u0026amp; LLM Inference at the Edge\u003c\/strong\u003e — Run quantised large language models and diffusion models locally with TensorRT-LLM, enabling private, on-premises AI assistants and content generation without sending sensitive data off-device.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAgricultural Robotics\u003c\/strong\u003e — Power multi-spectral crop-monitoring drones and harvesting robots that need vision, navigation, and actuation compute in a package capable of operating in field conditions.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eRetail \u0026amp; In-Store Analytics\u003c\/strong\u003e — Run anonymous people-counting, queue-length detection, and shelf-inventory AI simultaneously across multiple camera feeds without transmitting footage off-site.\u003c\/li\u003e\n    \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eRobotics Research \u0026amp; Rapid Prototyping\u003c\/strong\u003e — The full set of carrier board interfaces — PCIe, GPIO, MIPI CSI, USB, CAN, I2S — lets research teams attach virtually any sensor or actuator and iterate without custom PCB work, with Isaac ROS providing ready-made perception primitives.\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\n\u003cul style=\"margin:0 0 12px;padding-left:22px;line-height:1.8;color:#e0e0e0;\"\u003e\n    \u003cli\u003eJetson AGX Orin module (64 GB) with heatsink and fan on reference carrier board\u003c\/li\u003e\n    \u003cli\u003eAzureWave AW-CB375NF Wi-Fi 802.11ac + Bluetooth 5.0 M.2 module (pre-installed)\u003c\/li\u003e\n    \u003cli\u003e90 W USB PD Type-C power supply with regional cords (US, EU, UK)\u003c\/li\u003e\n    \u003cli\u003eUSB Type-A to Type-C cable\u003c\/li\u003e\n    \u003cli\u003eQuick Start Guide\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp style=\"font-size:13px;margin:0 0 20px;line-height:1.6;color:#a0a0a0;\"\u003e\u003cem\u003eNote: accessories such as NVMe SSDs, MIPI cameras, display adapters, and microSD cards are sold separately and not included unless stated above.\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 software and ML frameworks are compatible with the Jetson AGX Orin Developer Kit?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson AGX Orin runs the full \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA JetPack 6 SDK\u003c\/span\u003e stack, which includes \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eCUDA 12.6\u003c\/span\u003e, \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eTensorRT 10.3\u003c\/span\u003e, cuDNN 9.3, OpenCV, and VPI 3.2. It is fully compatible with \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eIsaac ROS\u003c\/span\u003e, DeepStream SDK, NVIDIA TAO Toolkit, and ROS 2 (Humble). Popular ML frameworks — PyTorch, TensorFlow, ONNX Runtime, and Triton Inference Server — all have validated ARM64 builds for Jetson. Because the Orin shares the same CUDA and TensorRT API versions as NVIDIA data-centre GPUs, models trained and optimised in the cloud can be deployed to the edge with minimal code changes.\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 are the power requirements for the Jetson AGX Orin Developer Kit?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe developer kit is powered by the included \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e90 W USB Power Delivery Type-C adapter\u003c\/span\u003e, which connects to the USB-C port on the carrier board. The module supports four software-selectable power modes: \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e15 W, 30 W, 50 W, and 60 W (MAXN)\u003c\/span\u003e, selectable via the nvpmodel utility or jtop. An alternative \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eDC barrel jack (7–20 V input)\u003c\/span\u003e is also present on the carrier board for integration into custom power systems. When building a production enclosure, account for peak wattage plus any peripherals connected to the PCIe slot or USB ports when sizing your 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;\"\u003eWhat operating system does the Jetson AGX Orin run?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson AGX Orin runs \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson Linux\u003c\/span\u003e (formerly L4T — Linux for Tegra), an Ubuntu-based distribution currently built on \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUbuntu 22.04 LTS\u003c\/span\u003e with Linux kernel 5.15. You flash it using \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e from a host PC running Ubuntu 20.04 or 22.04 — the developer kit does not ship pre-flashed. JetPack 6.x is recommended for all new projects; JetPack 5.x (Ubuntu 20.04 base) remains available for projects requiring legacy compatibility. Red Hat Device Edge is also tested and supported as an alternative OS for enterprise deployments.\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 does the Jetson AGX Orin Developer Kit support?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe module includes \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e64 GB eMMC 5.1\u003c\/span\u003e on-module flash for the operating system and core applications. For expanded storage, the carrier board provides an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eM.2 Key M slot (x4 PCIe Gen4)\u003c\/span\u003e supporting NVMe SSDs in the 2280 form factor — this is the recommended path for large datasets and model files, as Gen4 NVMe delivers far higher sequential throughput than the eMMC. A \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003emicroSD slot (UHS-1, SDR104 mode)\u003c\/span\u003e is also available for removable media. NVMe drives are not included in the box and are sold separately; a PCIe Gen4 NVMe 2280 SSD is strongly recommended for any serious inference or training workload.\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 do I need to get started with the Jetson AGX Orin Developer Kit?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eOut of the box you need: a display connected via \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eDisplayPort 1.4a\u003c\/span\u003e (or a DP-to-HDMI adapter, sold separately), a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eUSB keyboard and mouse\u003c\/span\u003e, and a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ehost PC running Ubuntu 20.04 or 22.04\u003c\/span\u003e for flashing JetPack via NVIDIA SDK Manager. The 90 W power supply and USB-C cable are included. For camera-based projects, USB cameras work immediately out of the box; MIPI CSI-2 cameras require an optional camera adapter board. An NVMe SSD (sold separately) is strongly recommended for working with large models or datasets beyond what the eMMC comfortably accommodates.\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 AGX Orin compare to the Jetson AGX Xavier?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe Jetson AGX Orin delivers \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eup to 8× more AI compute\u003c\/span\u003e than the AGX Xavier — 275 TOPS vs. 30 TOPS. The GPU steps from 512-core Volta to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e2048-core Ampere\u003c\/span\u003e with Tensor Cores and sparse-tensor support not available on Xavier. The CPU moves from 8-core Carmel to a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e12-core Cortex-A78AE at 2.2 GHz\u003c\/span\u003e, approximately 1.7× faster per-thread. Memory doubles to 64 GB LPDDR5 with 1.4× higher bandwidth than Xavier's LPDDR4x. Both modules share the same 699-pin SOM connector, so many Xavier-compatible carrier boards are physically compatible with Orin — though software must be updated to JetPack 5 or 6.\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 pins and interfaces does the 40-pin header provide?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe standard \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e40-pin expansion header\u003c\/span\u003e (2×20, 2.54 mm pitch, Raspberry Pi HAT-compatible pinout) provides I2C (×2), SPI (×2), UART (×2), I2S, CAN, GPIO, PWM, 3.3 V, and 5 V supply rails — exact pin allocation depends on the device-tree overlay loaded at boot. Beyond the 40-pin header, the carrier board adds a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e12-pin automation header\u003c\/span\u003e (SPI, DMIC, GPIO), a 10-pin audio header, a 10-pin JTAG debug header, a 4-pin fan header, and a 2-pin RTC battery connector. This combination lets you attach motor drivers, IMUs, CAN bus networks, and audio peripherals simultaneously without an I\/O hub.\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 AGX Orin Developer Kit suitable for beginners or only advanced users?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe developer kit targets professional developers, researchers, and advanced students — it is not a general-purpose single-board computer. Familiarity with Linux CLI, Python or C++, and a working knowledge of ML frameworks is expected. That said, NVIDIA provides free \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson AI Courses\u003c\/span\u003e on the NVIDIA Deep Learning Institute (DLI) covering JetPack setup, TensorRT optimisation, and Isaac ROS basics. The official \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eHello AI World\u003c\/span\u003e tutorial gets a real-time object-detection pipeline running in under an hour. Developers comfortable with Raspberry Pi who want to move into serious AI inference will find the learning curve worthwhile but should budget time for the initial JetPack flashing and SDK environment setup.\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 when setting up the Jetson AGX Orin for the first time?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe most frequent pitfall is attempting to flash JetPack from a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eWindows or macOS host\u003c\/span\u003e — NVIDIA SDK Manager only runs on x86_64 Ubuntu 20.04 or 22.04. A second common issue is forgetting to enter \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eForce Recovery mode\u003c\/span\u003e before connecting to the host PC — hold the Force Recovery button while pressing Power; the board will not appear as a USB device otherwise. Finally, many users do not expand the root partition after flashing and run out of space when installing JetPack components on the 64 GB eMMC — installing immediately to an \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSD\u003c\/span\u003e or running resize2fs on the eMMC partition right after first boot eliminates this issue entirely.\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 documentation, community support, and firmware updates for the Jetson AGX Orin?\u003c\/p\u003e\n    \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eOfficial documentation lives on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer portal\u003c\/span\u003e (developer.nvidia.com\/embedded), including the Jetson AGX Orin User Guide, JetPack release notes, and the Jetson Linux driver package. Firmware and JetPack updates are distributed via \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e (GUI installer) or the Jetson Linux archive for command-line flashing. Community support is active on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer Forums\u003c\/span\u003e under the Jetson \u0026amp; Embedded Systems category — most hardware and software questions have existing threads. The \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ejetson-containers\u003c\/span\u003e project on GitHub provides a curated library of ready-to-run Docker containers for LLMs, diffusion models, vision pipelines, and robotics workloads, saving significant environment-setup time.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":43037640130665,"sku":"NVD-015","price":225299.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/878f723d-be71-4a0d-9a3d-d37c1bb38709.jpg?v=1774270036","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-agx-orin-developer-kit64gb","provider":"EdgeTech Robotics","version":"1.0","type":"link"}