Nvidia Jetson AGX Xavier Module
NVIDIA Jetson AGX Xavier — 32 TOPS AI Inference — 512-Core Volta GPU — Industrial Edge AI Module The NVIDIA Jetson AGX Xavier delivers workstation-class AI compute in a palm-sized...
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NVIDIA Jetson AGX Xavier — 32 TOPS AI Inference — 512-Core Volta GPU — Industrial Edge AI Module
The 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 64GB, 32GB, and Industrial 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.
Key Highlights
- 32 TOPS AI Performance — 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.
- 512-Core Volta GPU with 64 Tensor Cores — Accelerates deep learning inference across vision, natural language, and multi-sensor fusion tasks simultaneously, using the same architecture as NVIDIA data-centre GPUs.
- 2× NVDLA Deep Learning Accelerators — Dedicated fixed-function engines offload neural network inference from the GPU, cutting latency and freeing GPU cycles for graphics and general compute.
- 8-Core NVIDIA Carmel ARMv8.2 CPU — 8 MB L2 + 4 MB L3 cache hierarchy sustains the multi-threaded control stacks, middleware, and OS workloads typical of production robotics systems.
- 256-Bit LPDDR4x at 136.5 GB/s — Industry-leading memory bandwidth prevents bottlenecks when multiple accelerators are streaming large activation maps and sensor data concurrently.
- Up to 36 Camera Virtual Channels — 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.
- 8× PCIe Gen4 High-Speed I/O — Direct attachment of NVMe SSDs, LiDAR controllers, radar modules, and FPGAs without throughput compromise, backed by 3× USB 3.1 for peripheral connectivity.
- Safety Cluster Engine (Industrial Only) — Two Arm Cortex-R5 cores operating in lockstep mode provide the functional safety supervision required for ASIL-compliant industrial and defence deployments.
- Configurable Power Profiles — 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.
- JetPack SDK & Full CUDA-X Ecosystem — Ships with TensorRT, cuDNN, DeepStream, and VisionWorks pre-optimised for the Volta architecture, enabling rapid model deployment from training to production inference.
Technical Specifications
| Specification | AGX Xavier 64GB | AGX Xavier 32GB | AGX Xavier Industrial |
| AI Performance | 32 TOPS | 30 TOPS | |
| GPU | NVIDIA Volta architecture — 512 CUDA Cores & 64 Tensor Cores | ||
| CPU | 8-Core NVIDIA Carmel ARMv8.2 64-bit — 8 MB L2 + 4 MB L3 | ||
| DL Accelerator | 2× NVDLA | ||
| Vision Accelerator | 2× PVA | ||
| Safety Cluster Engine | — | 2× Arm Cortex-R5 in lockstep | |
| Memory | 64 GB 256-bit LPDDR4x — 136.5 GB/s | 32 GB 256-bit LPDDR4x — 136.5 GB/s | 32 GB 256-bit LPDDR4x (ECC) — 136.5 GB/s |
| Storage | 32 GB eMMC 5.1 | 64 GB eMMC 5.1 | |
| Camera | Up 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) | Up 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) | |
| Video Encode | 4× 4K60 | 8× 4K30 | 16× 1080p60 | 32× 1080p30 (H.265) — 30× 1080p30 (H.264) | 2× 4K60 | 6× 4K30 | 12× 1080p60 | 24× 1080p30 (H.265/H.264) | |
| Video Decode | 2× 8K30 | 6× 4K60 | 12× 4K30 | 26× 1080p60 | 52× 1080p30 (H.265) — 30× 1080p30 (H.264) | 2× 8K30 | 4× 4K60 | 8× 4K30 | 18× 1080p60 | 36× 1080p30 (H.265) — 24× 1080p30 (H.264) | |
| UPHY | 8× PCIe Gen4 | 8× SLVS-EC — 3× USB 3.1 — Single Lane UFS | 8× PCIe Gen4 — 3× USB 3.1 — Single Lane UFS | |
| Power | 10W | 15W | 30W | 20W | 40W | |
| Networking | 10/100/1000 BASE-T Ethernet | ||
| Display | Three multi-mode DP 1.2 / eDP 1.4 / HDMI 2.0 a/b | ||
| Other I/O | USB 2.0 — UART, SPI, CAN, I2C, I2S, DMIC & DSPK, GPIOs | ||
| Operating Temperature | -25°C to 80°C (at TTP) | -40°C to 85°C (at TTP) | |
| Operating Lifetime | 5 Years | 10 Years | |
| Mechanical | 100 mm × 87 mm — 699-pin connector — Integrated Thermal Transfer Plate | ||
Which AGX Xavier Variant Is Right for You?
All 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.
| Factor | AGX Xavier 64GB | AGX Xavier 32GB | AGX Xavier Industrial |
| Memory | 64 GB LPDDR4x | 32 GB LPDDR4x | 32 GB LPDDR4x + ECC |
| Storage | 32 GB eMMC 5.1 | 64 GB eMMC 5.1 | |
| Power Profiles | 10W / 15W / 30W | 20W / 40W | |
| Operating Temp | -25°C to 80°C | -40°C to 85°C | |
| Safety Cluster | — | 2× Arm Cortex-R5 (lockstep) | |
| Operating Lifetime | 5 Years | 10 Years | |
| Best For | Large multi-model pipelines, LLM-at-edge, memory-intensive sensor fusion | Standard production robotics, AMR, smart cameras, research platforms | Harsh environments, safety-critical systems, defence, aerospace, factory automation |
Common Applications & Use Cases
- Autonomous Mobile Robots (AMR) — 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.
- Autonomous Ground Vehicles — 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.
- Industrial Automated Inspection — Runs multiple high-resolution defect detection models simultaneously for PCB assembly, weld quality, and surface finish inspection on high-speed production lines.
- Smart City & Traffic Analytics — 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.
- Medical Imaging & AI Diagnostics — 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.
- Drone & UAV Perception — 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.
- Logistics & Warehouse Automation — Powers pick-and-place robot vision, conveyor OCR, item dimensioning, and multi-arm coordination with the deterministic latency needed for high-throughput fulfilment centres.
- Defence & Aerospace Systems — 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.
- Precision Agriculture & Field Robotics — Drives multi-spectral sensor fusion, AI weed detection, and autonomous path following on tractors and field robots operating in unstructured outdoor environments.
- Research & Rapid Prototyping — 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.
What's in the Box
- 1× NVIDIA Jetson AGX Xavier Module (64GB, 32GB, or Industrial — as ordered)
Note: accessories such as power supplies, cables, cases, and SD cards are sold separately and not included unless stated above.
Frequently Asked Questions
What operating systems are compatible with the Jetson AGX Xavier?
The Jetson AGX Xavier runs NVIDIA JetPack SDK, 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 TensorRT, cuDNN, DeepStream, and VisionWorks. Custom Linux distributions are also supported provided a compatible device tree and BSP are used. Windows is not natively supported on this module.
What are the power requirements for the Jetson AGX Xavier?
The standard AGX Xavier modules (64GB and 32GB) operate across three selectable power modes: 10W, 15W, and 30W. The Industrial variant uses 20W and 40W 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.
How does the Jetson AGX Xavier compare to the Jetson AGX Orin?
The Jetson AGX Orin 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.
What storage options are available beyond the built-in eMMC?
In addition to the on-module 32 GB (64 GB on Industrial) eMMC 5.1, the AGX Xavier supports external NVMe SSDs via its PCIe Gen4 interface — 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.
What accessories are needed to start using the Jetson AGX Xavier module?
The bare module requires a compatible carrier board 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 9–20V DC power supply, 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.
Does the Jetson AGX Xavier support popular AI frameworks like PyTorch and TensorFlow?
Yes — PyTorch, TensorFlow, and ONNX Runtime 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 NVIDIA TensorRT, 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.
How many GPIO pins and communication interfaces does the Jetson AGX Xavier expose?
The AGX Xavier exposes a rich I/O set via its 699-pin board-to-board connector, 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, 3× USB 3.1, Gigabit Ethernet, and three display outputs (DP/eDP/HDMI) are available. The exact pins exposed at system level depend on the carrier board design.
Is the Jetson AGX Xavier module suitable for beginners or is it aimed at professional engineers?
The bare module is primarily aimed at professional embedded engineers and system integrators designing custom carrier boards for production hardware. Beginners and researchers are better served by the Jetson AGX Xavier Developer Kit, 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.
What is the most common mistake when deploying the Jetson AGX Xavier in production?
The most frequent issue is leaving the module on the default 15W power mode 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 TensorRT optimisation before deployment: running unoptimised PyTorch models can yield 3–4× lower throughput than the hardware is capable of. Always benchmark with jetson_clocks and a TensorRT-compiled engine before finalising your system design.
Where can I find official documentation, firmware updates, and community support?
The primary resources are the NVIDIA Jetson Linux Developer Guide at developer.nvidia.com/embedded, the NVIDIA Developer Forums (forums.developer.nvidia.com — Jetson & Embedded Systems section), and NVIDIA SDK Manager for downloading and flashing JetPack releases. The JetsonHacks community site and GitHub organisation also offer hardware teardowns, setup scripts, and project tutorials maintained by the broader Jetson community.
