{"product_id":"nvidia-jetson-agx-xavier-module","title":"Nvidia Jetson AGX Xavier Module","description":"\u003ch2 style=\"font-size:1.4em;font-weight:700;margin:0 0 12px;line-height:1.4;\"\u003eNVIDIA Jetson AGX Xavier — 32 TOPS AI Inference — 512-Core Volta GPU — Industrial Edge AI Module\u003c\/h2\u003e\n\u003cp style=\"margin:0 0 20px;line-height:1.7;color:#e0e0e0;\"\u003eThe NVIDIA Jetson AGX Xavier delivers workstation-class AI compute in a palm-sized 100×87 mm system-on-module, making fully autonomous machines practical at the edge. Available in \u003cstrong\u003e64GB\u003c\/strong\u003e, \u003cstrong\u003e32GB\u003c\/strong\u003e, and \u003cstrong\u003eIndustrial\u003c\/strong\u003e configurations, it scales from a power-efficient 10W mobile profile to a ruggedised 40W Industrial variant rated for -40°C to 85°C with ECC memory and a dedicated Safety Cluster Engine.\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\u003e32 TOPS AI Performance\u003c\/strong\u003e — Run demanding real-time inference at the edge without cloud dependency; the dual NVDLA engines plus 512-core Volta GPU work in parallel to sustain up to 32 trillion operations per second.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e512-Core Volta GPU with 64 Tensor Cores\u003c\/strong\u003e — Accelerates deep learning inference across vision, natural language, and multi-sensor fusion tasks simultaneously, using the same architecture as NVIDIA data-centre GPUs.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e2× NVDLA Deep Learning Accelerators\u003c\/strong\u003e — Dedicated fixed-function engines offload neural network inference from the GPU, cutting latency and freeing GPU cycles for graphics and general compute.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e8-Core NVIDIA Carmel ARMv8.2 CPU\u003c\/strong\u003e — 8 MB L2 + 4 MB L3 cache hierarchy sustains the multi-threaded control stacks, middleware, and OS workloads typical of production robotics systems.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e256-Bit LPDDR4x at 136.5 GB\/s\u003c\/strong\u003e — Industry-leading memory bandwidth prevents bottlenecks when multiple accelerators are streaming large activation maps and sensor data concurrently.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eUp to 36 Camera Virtual Channels\u003c\/strong\u003e — 16 MIPI CSI-2 lanes support up to 6 direct cameras or 36 virtual channels, enabling full-surround perception systems for autonomous vehicles and robotics platforms.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003e8× PCIe Gen4 High-Speed I\/O\u003c\/strong\u003e — Direct attachment of NVMe SSDs, LiDAR controllers, radar modules, and FPGAs without throughput compromise, backed by 3× USB 3.1 for peripheral connectivity.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eSafety Cluster Engine (Industrial Only)\u003c\/strong\u003e — Two Arm Cortex-R5 cores operating in lockstep mode provide the functional safety supervision required for ASIL-compliant industrial and defence deployments.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eConfigurable Power Profiles\u003c\/strong\u003e — Three selectable TDP modes (10W \/ 15W \/ 30W on standard variants; 20W \/ 40W on Industrial) let system designers tune compute density versus thermal budget for each deployment context.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eJetPack SDK \u0026amp; Full CUDA-X Ecosystem\u003c\/strong\u003e — Ships with TensorRT, cuDNN, DeepStream, and VisionWorks pre-optimised for the Volta architecture, enabling rapid model deployment from training to production inference.\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:560px;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;\"\u003eAGX Xavier 64GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAGX Xavier 32GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAGX Xavier Industrial\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eAI Performance\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e32 TOPS\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e30 TOPS\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eGPU\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"3\"\u003eNVIDIA Volta architecture — 512 CUDA Cores \u0026amp; 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;\" colspan=\"3\"\u003e8-Core NVIDIA Carmel ARMv8.2 64-bit — 8 MB L2 + 4 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;\" colspan=\"3\"\u003e2× NVDLA\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=\"3\"\u003e2× PVA\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;\"\u003eSafety Cluster Engine\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\"\u003e—\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2× Arm Cortex-R5 in lockstep\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 LPDDR4x — 136.5 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;\"\u003e32 GB 256-bit LPDDR4x — 136.5 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;\"\u003e32 GB 256-bit LPDDR4x (ECC) — 136.5 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\"\u003e32 GB eMMC 5.1\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\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eCamera\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003eUp to 6 cameras (36 via virtual channels) — 16 lanes MIPI CSI-2 | 8 lanes SLVS-EC — D-PHY 1.2 (up to 40 Gbps) — C-PHY 1.1 (up to 62 Gbps)\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 6 cameras (36 via virtual channels) — 16 lanes MIPI CSI-2 — D-PHY 1.2 (up to 40 Gbps) — C-PHY 1.1 (up to 62 Gbps)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eVideo Encode\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e4× 4K60 | 8× 4K30 | 16× 1080p60 | 32× 1080p30 (H.265) — 30× 1080p30 (H.264)\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 | 6× 4K30 | 12× 1080p60 | 24× 1080p30 (H.265\/H.264)\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eVideo Decode\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e2× 8K30 | 6× 4K60 | 12× 4K30 | 26× 1080p60 | 52× 1080p30 (H.265) — 30× 1080p30 (H.264)\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2× 8K30 | 4× 4K60 | 8× 4K30 | 18× 1080p60 | 36× 1080p30 (H.265) — 24× 1080p30 (H.264)\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;\"\u003eUPHY\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\"\u003e8× PCIe Gen4 | 8× SLVS-EC — 3× USB 3.1 — Single Lane UFS\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e8× PCIe Gen4 — 3× USB 3.1 — Single Lane UFS\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003ePower\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e10W | 15W | 30W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e20W | 40W\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=\"3\"\u003e10\/100\/1000 BASE-T 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;\"\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=\"3\"\u003eThree multi-mode DP 1.2 \/ eDP 1.4 \/ HDMI 2.0 a\/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;\"\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=\"3\"\u003eUSB 2.0 — UART, SPI, CAN, I2C, I2S, 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;\"\u003eOperating Temperature\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\"\u003e-25°C to 80°C (at TTP)\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e-40°C to 85°C (at TTP)\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;\"\u003eOperating Lifetime\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\"\u003e5 Years\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e10 Years\u003c\/td\u003e\n    \u003c\/tr\u003e\n    \u003ctr\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eMechanical\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"3\"\u003e100 mm × 87 mm — 699-pin connector — Integrated 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 Xavier Variant Is Right for You?\u003c\/h3\u003e\n\u003cp style=\"margin:0 0 16px;line-height:1.7;color:#e0e0e0;\"\u003eAll three modules share the same Volta GPU, CPU, and accelerator core but differ in memory capacity, storage, power envelope, and environmental ratings. Choose by your memory headroom requirement and deployment environment.\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;\"\u003eFactor\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAGX Xavier 64GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAGX Xavier 32GB\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:2px solid #3a3a3a;font-weight:700;color:#BAFF02;\"\u003eAGX Xavier Industrial\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 LPDDR4x\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 LPDDR4x\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 LPDDR4x + ECC\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\"\u003e32 GB eMMC 5.1\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\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 Profiles\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\" colspan=\"2\"\u003e10W \/ 15W \/ 30W\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e20W \/ 40W\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;\"\u003eOperating Temp\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\"\u003e-25°C to 80°C\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e-40°C to 85°C\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;\"\u003eSafety Cluster\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\"\u003e—\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e2× Arm Cortex-R5 (lockstep)\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;\"\u003eOperating Lifetime\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\"\u003e5 Years\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border-bottom:1px solid #3a3a3a;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003e10 Years\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;\"\u003eLarge multi-model pipelines, LLM-at-edge, memory-intensive sensor fusion\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eStandard production robotics, AMR, smart cameras, research platforms\u003c\/td\u003e\n      \u003ctd style=\"padding:10px 12px;border:0;font-weight:600;word-wrap:break-word;color:#e0e0e0;\"\u003eHarsh environments, safety-critical systems, defence, aerospace, factory automation\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 (AMR)\u003c\/strong\u003e — Real-time simultaneous localisation, sensor fusion, path planning, and obstacle avoidance all run concurrently on a single module, eliminating the need for a separate compute stack.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eAutonomous Ground Vehicles\u003c\/strong\u003e — High-bandwidth MIPI CSI-2 and PCIe Gen4 interfaces let the module ingest LiDAR, radar, and camera data in parallel while running full self-driving perception and planning networks.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eIndustrial Automated Inspection\u003c\/strong\u003e — Runs multiple high-resolution defect detection models simultaneously for PCB assembly, weld quality, and surface finish inspection on high-speed production lines.\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 — Up to 36 virtual camera channels combined with DeepStream SDK enable dense multi-camera vehicle counting, incident detection, and pedestrian flow analysis in real time.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eMedical Imaging \u0026amp; AI Diagnostics\u003c\/strong\u003e — ECC memory on the Industrial variant ensures bit-error protection for diagnostic accuracy; the module's inference throughput supports real-time radiology AI at the point of care.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDrone \u0026amp; UAV Perception\u003c\/strong\u003e — The compact 100 × 87 mm footprint and 10W minimum power mode make the Jetson AGX Xavier viable for weight- and power-constrained aerial platforms requiring onboard inference.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eLogistics \u0026amp; Warehouse Automation\u003c\/strong\u003e — Powers pick-and-place robot vision, conveyor OCR, item dimensioning, and multi-arm coordination with the deterministic latency needed for high-throughput fulfilment centres.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eDefence \u0026amp; Aerospace Systems\u003c\/strong\u003e — The Industrial variant's 50G operational shock rating, 5G RMS vibration tolerance, and -40°C to 85°C range satisfy requirements for airborne ISR platforms and field-deployed systems.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003ePrecision Agriculture \u0026amp; Field Robotics\u003c\/strong\u003e — Drives multi-spectral sensor fusion, AI weed detection, and autonomous path following on tractors and field robots operating in unstructured outdoor environments.\u003c\/li\u003e\n  \u003cli style=\"margin-bottom:14px;padding-left:0;line-height:1.6;\"\u003e\n\u003cstrong\u003eResearch \u0026amp; Rapid Prototyping\u003c\/strong\u003e — Full JetPack SDK compatibility and CUDA-X access let teams go from PyTorch or TensorFlow training to optimised TensorRT deployment without leaving the Jetson ecosystem.\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 Xavier Module (64GB, 32GB, or Industrial — as ordered)\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: accessories such as power supplies, cables, cases, and SD 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 operating systems are compatible with 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 Xavier runs \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA JetPack SDK\u003c\/span\u003e, which is built on Ubuntu Linux (18.04 for JetPack 4.x, 20.04 for JetPack 5.x). JetPack bundles the full BSP, kernel, and all CUDA-X libraries including \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eTensorRT, cuDNN, DeepStream, and VisionWorks\u003c\/span\u003e. Custom Linux distributions are also supported provided a compatible device tree and BSP are used. Windows is not natively supported on this module.\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 Xavier?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe standard AGX Xavier modules (64GB and 32GB) operate across three selectable power modes: \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e10W, 15W, and 30W\u003c\/span\u003e. The Industrial variant uses \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e20W and 40W\u003c\/span\u003e profiles. Input voltage is supplied through the carrier board — the NVIDIA reference carrier board accepts 9–20V DC. The module itself requires a carrier board for power delivery and does not have a direct mains connector.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eHow does the Jetson AGX Xavier compare to the Jetson AGX Orin?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson AGX Orin\u003c\/span\u003e is NVIDIA's next-generation platform and delivers up to 275 TOPS — roughly 8× the AI performance of the AGX Xavier's 32 TOPS. Orin uses the Ampere GPU architecture and introduces 12 Arm Cortex-A78AE CPU cores in place of Xavier's 8-core Carmel design. For new designs prioritising peak inference throughput, Orin is the recommended platform; the AGX Xavier remains an excellent choice for production programmes where it is already qualified and cost-efficiency is the priority.\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 beyond the built-in eMMC?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eIn addition to the on-module \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e32 GB (64 GB on Industrial) eMMC 5.1\u003c\/span\u003e, the AGX Xavier supports external NVMe SSDs via its \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ePCIe Gen4 interface\u003c\/span\u003e — most carrier boards expose an M.2 Key M slot for this purpose. A Single Lane UFS interface is also available for additional flash storage. SD cards are not directly supported by the module itself, though some carrier boards add an SD slot via their own controller. NVMe is the recommended choice for high-throughput data logging and large dataset storage.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eWhat accessories are needed to start using the Jetson AGX Xavier module?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe bare module requires a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ecompatible carrier board\u003c\/span\u003e that breaks out its 699-pin connector interfaces — the NVIDIA Jetson AGX Xavier Developer Kit carrier board is the most common starting point. You will also need a \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e9–20V DC power supply\u003c\/span\u003e, a USB-C cable for flashing, a display (HDMI or DisplayPort), and a host PC running Ubuntu with NVIDIA SDK Manager for initial setup. Camera modules, Ethernet cables, and NVMe SSDs are optional but typical for most applications.\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 AGX Xavier support popular AI frameworks like PyTorch and TensorFlow?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eYes — \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ePyTorch, TensorFlow, and ONNX Runtime\u003c\/span\u003e all run on Jetson AGX Xavier via JetPack's CUDA-X libraries. Models are typically trained on a desktop or cloud GPU, then exported and optimised for inference using \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA TensorRT\u003c\/span\u003e, which can achieve 2–4× throughput improvement over unoptimised frameworks on the same hardware. The NVIDIA NGC catalogue also provides pre-trained, optimised model containers ready to deploy directly on Jetson.\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 communication interfaces does the Jetson AGX Xavier expose?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe AGX Xavier exposes a rich I\/O set via its \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e699-pin board-to-board connector\u003c\/span\u003e, including GPIO, UART, SPI, CAN, I2C, I2S, DMIC, and DSPK interfaces. The reference carrier board breaks out a 40-pin header compatible with Raspberry Pi HATs for GPIO access. On top of this, \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003e3× USB 3.1, Gigabit Ethernet, and three display outputs\u003c\/span\u003e (DP\/eDP\/HDMI) are available. The exact pins exposed at system level depend on the carrier board design.\u003c\/p\u003e\n\u003c\/div\u003e\n\n\u003cdiv style=\"background:#1a1a1a;border-left:3px solid #BAFF02;border-radius:4px;padding:18px 20px;margin:0 0 12px;\"\u003e\n  \u003cp style=\"font-weight:700;color:#BAFF02;margin:0 0 10px;line-height:1.5;font-size:0.97em;\"\u003eIs the Jetson AGX Xavier module suitable for beginners or is it aimed at professional engineers?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe bare module is primarily aimed at \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eprofessional embedded engineers and system integrators\u003c\/span\u003e designing custom carrier boards for production hardware. Beginners and researchers are better served by the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetson AGX Xavier Developer Kit\u003c\/span\u003e, which includes the carrier board, cooling solution, and power supply needed to get started immediately. Once comfortable with the JetPack ecosystem and CUDA development, transitioning to the production module is straightforward.\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 deploying the Jetson AGX Xavier in production?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe most frequent issue is leaving the module on the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003edefault 15W power mode\u003c\/span\u003e without validating whether a higher TDP is needed — many deployments underperform simply because the power profile was never changed from factory default. A close second is skipping \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eTensorRT optimisation\u003c\/span\u003e before deployment: running unoptimised PyTorch models can yield 3–4× lower throughput than the hardware is capable of. Always benchmark with \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003ejetson_clocks\u003c\/span\u003e and a TensorRT-compiled engine before finalising your system 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?\u003c\/p\u003e\n  \u003cp style=\"margin:0;line-height:1.75;font-size:0.94em;color:#e0e0e0;\"\u003eThe primary resources are the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Jetson Linux Developer Guide\u003c\/span\u003e at developer.nvidia.com\/embedded, the \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA Developer Forums\u003c\/span\u003e (forums.developer.nvidia.com — Jetson \u0026amp; Embedded Systems section), and \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eNVIDIA SDK Manager\u003c\/span\u003e for downloading and flashing JetPack releases. The \u003cspan style=\"color:#BAFF02;font-weight:600;\"\u003eJetsonHacks\u003c\/span\u003e community site and GitHub organisation also offer hardware teardowns, setup scripts, and project tutorials maintained by the broader Jetson community.\u003c\/p\u003e\n\u003c\/div\u003e\n","brand":"Nvidia","offers":[{"title":"32GB","offer_id":43061978038377,"sku":"NVD-009","price":140749.99,"currency_code":"INR","in_stock":true},{"title":"Industrial","offer_id":43061978103913,"sku":"NVD-011","price":195999.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0672\/4229\/5401\/files\/buy-jetson_agx_xavier_module--XavierModule_White.jpg?v=1774506155","url":"https:\/\/edgetechrobotics.com\/products\/nvidia-jetson-agx-xavier-module","provider":"EdgeTech Robotics","version":"1.0","type":"link"}