{"product_id":"nvidia-jetson-agx-orin-module","title":"NVIDIA Jetson AGX Orin Module","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;\"\u003eNVIDIA Jetson AGX Orin™ Module — Up to 275 TOPS AI Performance — Ampere Architecture — 32GB \u0026amp; 64GB LPDDR5\u003c\/h2\u003e\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe \u003cstrong\u003eNVIDIA Jetson AGX Orin™\u003c\/strong\u003e is the highest-performance module in the Jetson Orin family, delivering up to \u003cstrong\u003e275 TOPS\u003c\/strong\u003e of INT8 AI compute in a compact 100 × 87 mm form factor pin-compatible with Jetson AGX Xavier. Available in 32GB and 64GB LPDDR5 configurations, it is purpose-built for robotics, autonomous machines, and industrial edge AI at the highest performance tier.\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 275 TOPS AI Performance\u003c\/strong\u003e — Delivers more than 8× the AI compute of Jetson AGX Xavier, enabling the most demanding real-time inference workloads at the edge without any cloud dependency.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eNVIDIA Ampere Architecture GPU\u003c\/strong\u003e — Up to 2048 CUDA cores and 64 Tensor Cores at 1.3 GHz, enabling concurrent execution of deep learning, computer vision, and graphics pipelines in a single module.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDual NVDLA v2.0 Deep Learning Accelerators\u003c\/strong\u003e — Two dedicated hardware engines offload neural network inference from the GPU, increasing throughput and power efficiency for always-on inference tasks.\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 — Up to 64GB of unified 256-bit LPDDR5 at 204.8 GB\/s provides ample headroom for large model weights and high-resolution sensor data without memory bottlenecks.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMulti-Camera \u0026amp; Multi-Sensor Input\u003c\/strong\u003e — Supports up to 6 simultaneous cameras (16 via virtual channels) over 16 MIPI CSI-2 lanes with D-PHY 2.1 \/ C-PHY 2.0, ideal for 360° perception systems.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003ePCIe Gen4 High-Speed I\/O\u003c\/strong\u003e — Up to 2×x8, 1×x4, and 2×x1 PCIe Gen4 lanes enable direct attachment of NVMe storage, FPGAs, accelerator cards, and high-speed radio modules.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eHardware Video Engine\u003c\/strong\u003e — Dedicated encode and decode engines handle up to 2×4K60 encode and 3×4K60 decode (64GB) with H.265, H.264, AV1, and VP9, freeing the GPU entirely for inference.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eFull NVIDIA AI Software Stack\u003c\/strong\u003e — Ships with JetPack SDK and is compatible with Isaac, DeepStream, Riva, TAO Toolkit, and Omniverse Replicator — all optimised for Orin's hardware accelerators.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eConfigurable Power Envelope\u003c\/strong\u003e — Scalable from 15W to 60W (64GB) lets you tune the performance-power trade-off for battery-operated or thermally constrained deployments.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDrop-In Xavier Compatibility\u003c\/strong\u003e — The 699-pin Molex Mirror Mezz connector and 100 × 87 mm footprint are pin-compatible with Jetson AGX Xavier, simplifying hardware migration for existing product designs.\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;\"\u003eJetson AGX Orin™ 32GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eJetson AGX Orin™ 64GB\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;\"\u003e200 TOPS (INT8)\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e275 TOPS (INT8)\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;\"\u003e1792-core NVIDIA Ampere GPU with 56 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;\"\u003e2048-core NVIDIA Ampere GPU with 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;\"\u003eGPU Max Frequency\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e930 MHz\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1.3 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;\"\u003e8-core Arm® Cortex®-A78AE v8.2 64-bit (2MB L2 + 4MB L3)\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 (3MB L2 + 6MB 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;\"\u003eCPU Max Frequency\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2.2 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;\"\u003e32GB 256-bit LPDDR5 — 204.8 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;\"\u003e64GB 256-bit LPDDR5 — 204.8 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\"\u003e64GB eMMC 5.1\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;\"\u003eDL Accelerator\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× NVDLA 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;\"\u003eDLA Max Frequency\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1.4 GHz\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1.6 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;\"\u003eVision Accelerator\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e1× PVA v2\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eCSI Camera\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003eUp to 6 cameras (16 via virtual channels) — 16 MIPI CSI-2 lanes — D-PHY 2.1 \/ C-PHY 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;\"\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      \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, 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      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1×8K30, 3×4K60, 7×4K30, 11×1080p60, 22×1080p30 (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;\"\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\"\u003eUp to 2×x8, 1×x4, 2×x1 PCIe Gen4 (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), 4× 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, 1× 10GbE\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× 8K60 multi-mode DP 1.4a \/ 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;\"\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\"\u003e4× UART, 3× SPI, 4× I2S, 8× I2C, 2× CAN, PWM, DMIC \u0026amp; DSPK, 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;\"\u003e15W – 40W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e15W – 60W\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;\"\u003eForm Factor\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\"\u003e100mm × 87mm — 699-pin Molex Mirror Mezz Connector\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;\"\u003eThermal\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003eIntegrated Thermal Transfer Plate\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 AGX Orin Module Is Right for You?\u003c\/h3\u003e\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eBoth modules share the same PCB footprint, connector, and I\/O configuration — the choice comes down to AI throughput, CPU core count, and memory capacity. The 32GB is well-suited for most single-domain robotics workloads, while the 64GB unlocks simultaneous multi-domain inference, larger transformer models, and higher-resolution multi-camera video pipelines.\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;\"\u003eCriteria\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eJetson AGX Orin™ 32GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eJetson AGX Orin™ 64GB\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;\"\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;\"\u003e275 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 \/ Frequency\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e1792 cores @ 930 MHz\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2048 cores @ 1.3 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 Cores\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 Arm A78AE\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 A78AE\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;\"\u003e32GB LPDDR5\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e64GB 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;\"\u003eMax Power\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e40W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e60W\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 (max)\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 \/ 12×1080p30\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 \/ 16×1080p30\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-domain robotics, AMRs, industrial inspection\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMulti-domain AI, large model inference, autonomous vehicles, multi-camera analytics\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 Mobile Robots (AMRs)\u003c\/strong\u003e — Simultaneous navigation, obstacle avoidance, and object detection pipelines run concurrently using the GPU and DLA engines, enabling real-time operation without cloud dependence.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e3D Perception \u0026amp; LiDAR Fusion\u003c\/strong\u003e — High memory bandwidth and the PVA vision accelerator handle dense point clouds and multi-sensor fusion for depth estimation and scene reconstruction at robotics frame rates.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eIndustrial Quality Inspection\u003c\/strong\u003e — Multi-camera input and NVDLA accelerators power high-throughput defect detection on production lines, replacing multiple dedicated vision processors with a single module.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMulti-Channel Video Analytics\u003c\/strong\u003e — Hardware decoders handle up to 22 simultaneous 1080p30 streams (64GB), enabling large-scale intelligent camera networks with per-stream AI analytics via DeepStream.\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 — Sufficient compute and memory for running mid-size transformer models and large language model inference locally, enabling voice assistants and conversational AI without cloud latency.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAutonomous Vehicles \u0026amp; Drones\u003c\/strong\u003e — Combines real-time video encode, multi-sensor fusion, and path planning in a power-constrained form factor for next-generation UAVs and ground vehicles.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMedical \u0026amp; Surgical Robotics\u003c\/strong\u003e — Deterministic AI performance and broad I\/O connectivity support image-guided intervention systems, endoscopy AI, and real-time surgical assistance applications.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSmart Retail \u0026amp; Occupancy Analytics\u003c\/strong\u003e — DeepStream-based multi-camera pipelines perform crowd analytics, shelf monitoring, and checkout automation without streaming video off-device.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSynthetic Data Generation\u003c\/strong\u003e — Omniverse Replicator integration lets teams generate labelled training datasets directly on Jetson hardware, reducing cloud data pipeline complexity for model development.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eEdge AI Inference Server\u003c\/strong\u003e — PCIe Gen4 and 10GbE allow the module to serve multiple upstream devices as a centralised on-premise inference endpoint, replacing rack-mounted GPU servers in constrained environments.\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 AGX Orin™ Module (32GB or 64GB, as ordered)\u003c\/li\u003e\n  \u003cli\u003eIntegrated Thermal Transfer Plate (factory-attached to module)\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 is required to use this module and is not included. Accessories such as power supplies, cables, cases, and storage devices 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;\"\u003eIs the Jetson AGX Orin module compatible with existing Jetson AGX Xavier carrier boards?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eYes. The AGX Orin uses the same \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e699-pin Molex Mirror Mezz connector\u003c\/span\u003e and \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e100 × 87 mm footprint\u003c\/span\u003e as the Jetson AGX Xavier, making it a drop-in module replacement on most existing AGX Xavier carrier boards. You should confirm with your carrier board manufacturer that power delivery and BSP support have been validated for AGX Orin, as some older designs may require a firmware update or minor hardware revision. NVIDIA's official Developer Kit carrier board is fully validated for both generations out of the box.\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 AGX Orin module require?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe AGX Orin module receives power through its carrier board rather than a direct onboard connector. The 32GB variant operates between \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e15W and 40W\u003c\/span\u003e, while the 64GB variant can draw up to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e60W\u003c\/span\u003e. When using the official Jetson AGX Orin Developer Kit carrier board, a compatible DC power supply (typically 19V input, rated for your TDP tier) is required and sold separately. Always consult your carrier board documentation for the exact connector type, voltage, and current rating. Running the module at lower power modes (e.g. 15W) is useful for thermally constrained or battery-powered enclosures.\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 and software stack does the Jetson AGX Orin support?\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;\"\u003eUbuntu Linux\u003c\/span\u003e via NVIDIA's \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetPack SDK\u003c\/span\u003e, which bundles the BSP, CUDA, cuDNN, TensorRT, and multimedia APIs into a single flashable image. JetPack 6.x (based on Ubuntu 22.04) is the current recommended release for AGX Orin. The full NVIDIA AI software stack — including Isaac SDK, DeepStream, Riva, TAO Toolkit, and Triton Inference Server — is available and optimised for Orin's hardware accelerators. Windows and macOS are not supported; the module is Linux-only.\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, and can I add an NVMe SSD?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eBoth AGX Orin modules include \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e64GB eMMC 5.1\u003c\/span\u003e soldered on-module for the root filesystem. For expanded storage, the carrier board's PCIe Gen4 interface supports high-speed \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVMe SSDs via M.2 Key M\u003c\/span\u003e — available on the official Developer Kit carrier board. You can configure the system to boot directly from NVMe if your carrier board and BSP support it, which is strongly recommended for workloads involving high-throughput data logging or large datasets. The eMMC is sufficient for the OS and applications, but external NVMe significantly extends storage capacity and write endurance.\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 start using the Jetson AGX Orin module?\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, a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eDC power supply\u003c\/span\u003e rated for your TDP tier, and either a monitor or SSH access for initial setup. For the quickest start, NVIDIA's Jetson AGX Orin Developer Kit includes a full-featured carrier board with all standard ports exposed. Additional recommended accessories include a USB keyboard and mouse, a DisplayPort or HDMI monitor, an NVMe SSD for expanded storage, and a USB-A to Micro-B cable for flashing via NVIDIA SDK Manager. Camera modules, sensors, and enclosures are application-specific and sold separately.\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 AGX Orin delivers up to \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e8× more AI performance\u003c\/span\u003e than the Jetson AGX Xavier (275 TOPS vs approximately 32 TOPS), thanks to the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Ampere GPU architecture\u003c\/span\u003e, upgraded NVDLA v2.0 accelerators, a faster Arm Cortex-A78AE CPU cluster, and LPDDR5 memory with higher bandwidth. It adds AV1 codec support, C-PHY 2.0 CSI, and PCIe Gen4 — all absent on Xavier. Critically, it retains the same connector and form factor, making hardware migration straightforward for existing Xavier-based product designs.\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, UART, I2C, SPI, and CAN interfaces does the module expose?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eVia the 699-pin connector, the AGX Orin exposes \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e4× UART, 3× SPI, 4× I2S, 8× I2C, 2× CAN, PWM, DMIC, DSPK, and multiple GPIOs\u003c\/span\u003e — the exact signals available at your system depend on how your carrier board routes them. Compared to the smaller Orin NX series, the AGX Orin provides substantially richer peripheral connectivity for complex embedded designs with multiple sensors and actuators. The 16-lane MIPI CSI-2 interface supports both D-PHY 2.1 and C-PHY 2.0 physical layers for maximum camera flexibility.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eIs this module suitable for beginners, or is it aimed at experienced embedded developers?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe AGX Orin module is primarily aimed at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eprofessional and industrial developers\u003c\/span\u003e building production-grade embedded AI systems. It does not include a carrier board, display, or power supply, requiring knowledge of embedded Linux, BSP flashing via \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e, and hardware bring-up. Beginners are better served by the Jetson AGX Orin Developer Kit or the Jetson Orin Nano Super Developer Kit, which include carrier boards and are ready to use out of the box. The module format is intended for integration into custom hardware products.\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 integrating the AGX Orin into a custom carrier board?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe most frequent issue is under-specifying the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003epower delivery circuitry\u003c\/span\u003e on the carrier board. The AGX Orin can draw up to 60W at peak, and instantaneous transient currents during GPU burst workloads can spike significantly higher — insufficient bulk capacitance or undersized power rails cause instability and unexpected reboots. A second common pitfall is neglecting to apply a proper \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ethermal interface material (TIM)\u003c\/span\u003e between the module's Thermal Transfer Plate and the system heatsink or chassis. NVIDIA's carrier board design guide and the AGX Orin module datasheet both provide detailed power sequencing and thermal guidelines that must be followed precisely during hardware 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 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 for the Jetson AGX Orin?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eAll official documentation — including the module datasheet, design guide, JetPack release notes, and BSP source — is available at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003edeveloper.nvidia.com\/embedded\/jetson-agx-orin\u003c\/span\u003e. Firmware and JetPack SDK updates are distributed through \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e and the NVIDIA L4T (Linux for Tegra) apt repository. The NVIDIA Developer Forums at forums.developer.nvidia.com host an active Jetson community for technical support, driver patches, and community-contributed projects. The jetson-inference GitHub repository maintained by Dusty Franklin provides practical inference containers and demos optimised for the Orin platform.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"32GB","offer_id":43037092184169,"sku":"NVD-012","price":140749.99,"currency_code":"INR","in_stock":true},{"title":"64GB","offer_id":43037092216937,"sku":"NVD-013","price":247799.99,"currency_code":"INR","in_stock":true},{"title":"Industrial","offer_id":43062001303657,"sku":"NVD-014","price":354899.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/51f01800-21b5-4b9e-9cad-afcba4258b97.jpg?v=1774265778","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-agx-orin-module","provider":"EdgeTech Robotics","version":"1.0","type":"link"}