NVIDIA Jetson Xavier NX Module
NVIDIA Jetson Xavier NX — 21 TOPS Edge AI — 384-Core Volta GPU — 8GB & 16GB LPDDR4x The NVIDIA Jetson Xavier NX Module is a production-ready AI System-on-Module (SoM)...
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NVIDIA Jetson Xavier NX — 21 TOPS Edge AI — 384-Core Volta GPU — 8GB & 16GB LPDDR4x
The NVIDIA Jetson Xavier NX Module is a production-ready AI System-on-Module (SoM) that delivers server-class Xavier SoC performance in a form factor smaller than a credit card — 69.6 × 45 mm. Available in 8GB and 16GB LPDDR4x configurations, it connects to any Xavier NX-compatible carrier board via the standard 260-pin SO-DIMM interface, giving embedded engineers, OEMs, and research teams a proven path to full-AI edge systems with up to 21 TOPS, dual NVDLA engines, hardware-accelerated video encode/decode, and multi-camera sensor fusion.
Key Highlights
- 21 TOPS AI Performance at the Edge — The Xavier NX delivers 21 TOPS of accelerated computing at 15W or 20W, and up to 14 TOPS at 10W — enabling real-time parallel neural network inference across object detection, segmentation, and video analytics entirely without cloud dependency.
- NVIDIA Volta GPU with 48 Tensor Cores — 384 CUDA cores and 48 Tensor Cores on the Volta architecture provide hardware-accelerated CUDA inference and TensorRT-optimised throughput on INT8 and FP16 workloads — purpose-built for production AI pipelines.
- 6-Core Carmel ARM CPU — The 6-core NVIDIA Carmel ARMv8.2 64-bit CPU with 6MB L2 + 4MB L3 cache runs Linux OS management, peripheral I/O, and application logic in parallel with GPU inference — without resource contention or scheduling delays.
- Dual NVDLA Deep Learning Accelerators — Two dedicated NVDLA engines offload specific inference workloads entirely from the GPU, enabling simultaneous multi-model execution where one network runs on NVDLA while another occupies the Volta GPU — maximising hardware utilisation per watt.
- Hardware Video Encode & Decode — Dedicated NVENC and NVDEC engines handle up to 2× 4K60 H.265 encoding and 2× 8K30 decoding simultaneously — freeing CPU and GPU entirely for AI inference while multi-stream video pipelines run in dedicated silicon.
- Multi-Camera CSI Pipeline — Up to 6 simultaneous cameras (expandable to 24 via virtual channels) over 14-lane MIPI CSI-2 with D-PHY 1.2 at up to 30 Gbps aggregate bandwidth — purpose-built for surround-view robotics, multi-sensor inspection, and autonomous machine vision.
- High-Speed PCIe Gen4 Expansion — One PCIe Gen3 ×1 plus one PCIe Gen4 ×4 lane (144 GT/s total) allow direct attachment of NVMe SSDs, Wi-Fi 6 adapters, FPGA co-processors, and AI accelerator cards with no USB overhead or bandwidth bottleneck.
- Onboard 16GB eMMC 5.1 Storage — Built-in eMMC means the module boots without any external SSD or SD card — simplifying BOM, reducing carrier board complexity, and enabling clean production deployments with a single solid-state storage solution.
- Configurable Power Envelopes — Selectable 10W, 15W, and 20W power modes let you balance peak AI throughput against thermal budget — critical for passively cooled enclosures, battery-powered field systems, and fanless industrial designs.
- Full JetPack & CUDA Ecosystem — Production-ready support for CUDA, TensorRT, cuDNN, PyTorch, TensorFlow, and NVIDIA JetPack SDK means existing AI models deploy with minimal porting effort — cloud-native container support further accelerates edge deployment pipelines.
Technical Specifications
| Specification | Xavier NX 8GB | Xavier NX 16GB |
| AI Performance | 21 TOPS | |
| GPU | 384-core NVIDIA Volta GPU — 48 Tensor Cores | |
| CPU | 6-core NVIDIA Carmel ARMv8.2 64-bit — 6MB L2 + 4MB L3 | |
| Memory | 8GB 128-bit LPDDR4x — 59.7 GB/s | 16GB 128-bit LPDDR4x — 59.7 GB/s |
| Storage | 16GB eMMC 5.1 (onboard) | |
| DL Accelerator | 2× NVDLA Engines | |
| Vision Accelerator | 7-Way VLIW Vision Processor | |
| Power | 10W | 15W | 20W | |
| PCIe | 1×1 PCIe Gen3 + 1×4 PCIe Gen4 — 144 GT/s total | |
| CSI Camera | Up to 6 cameras (24 via virtual channels) — 14-lane MIPI CSI-2, D-PHY 1.2 up to 30 Gbps | |
| Video Encode | 2× 4K60 | 4× 4K30 | 10× 1080p60 | 22× 1080p30 (H.265) | |
| Video Decode | 2× 8K30 | 6× 4K60 | 12× 4K30 | 22× 1080p60 | 44× 1080p30 (H.265) | |
| Display | 2× multi-mode DP 1.4 / eDP 1.4 / HDMI 2.0 | |
| Networking | 10/100/1000 BASE-T Ethernet | |
| Mechanical | 69.6 × 45 mm — 260-pin SO-DIMM connector | |
Which Jetson Xavier NX Is Right for You?
Both Xavier NX variants are built on the same Xavier SoC and deliver identical 21 TOPS AI performance — the decision is purely about memory headroom for your neural network pipeline. The 8GB module comfortably handles single or dual-model workloads and most standard embedded AI deployments. Choose the 16GB module when running large transformer models, concurrent multi-model inference, or high-resolution multi-stream video analytics where 8GB becomes the bottleneck — memory is soldered and cannot be upgraded after purchase.
| Feature | Xavier NX 8GB | Xavier NX 16GB |
| RAM | 8GB 128-bit LPDDR4x | 16GB 128-bit LPDDR4x |
| Memory Bandwidth | 59.7 GB/s (identical across both variants) | |
| AI Performance | 21 TOPS (identical across both variants) | |
| GPU & CPU | Identical across both variants | |
| Power Range | 10W | 15W | 20W (identical across both variants) | |
| Best For | Single/dual-model inference, standard multi-camera deployments, most embedded AI applications | Large models, multi-model concurrent inference, 4K multi-stream analytics, future-proofed OEM designs |
Common Applications & Use Cases
- Commercial Robotics & Autonomous Machines — Run simultaneous perception, planning, and navigation models on a single Xavier NX module using the Volta GPU, dual NVDLA engines, and multi-camera CSI pipeline — purpose-built for the demanding real-time workloads of mobile robots and autonomous guided vehicles.
- Automated Optical Inspection (AOI) — High-throughput defect detection on industrial production lines benefits directly from hardware-accelerated 6-camera CSI capture, TensorRT inference on Volta, and dedicated NVDLA offload — delivering sub-millisecond decision latency without cloud round-trips.
- Medical Instruments & Diagnostic Imaging — Certified production-ready hardware with defined power envelopes and consistent compute performance makes the Xavier NX suitable for AI-assisted imaging devices, surgical robotics, and bedside diagnostic tools requiring regulatory-grade reliability.
- Smart Cameras & Intelligent Vision Systems — Embedded AI directly in the camera node eliminates central compute bottlenecks — the Xavier NX processes inference, encodes video in hardware, and communicates results over Ethernet or PCIe in a single compact thermal envelope.
- High-Resolution Multi-Sensor Fusion — Up to 6 simultaneous high-resolution CSI cameras, dual NVDLA engines, and the Volta GPU enable complex multi-modal sensor fusion tasks in autonomous vehicles, drones, and advanced driver-assistance systems (ADAS).
- Smart Factory & Industry 4.0 Systems — Deploy predictive maintenance, worker safety monitoring, and real-time quality control AI directly on the factory floor using the Xavier NX's sealed SO-DIMM form factor, fanless-friendly power modes, and industrial I/O interfaces.
- Edge Video Analytics at Scale — Simultaneously decode up to 44× 1080p30 streams in hardware and run AI inference without GPU involvement in decoding — making the Xavier NX ideal for large-scale smart city, retail, and security monitoring deployments.
- Drone & UAV Payloads — The 10W low-power mode and compact 69.6 × 45 mm footprint enable integration into weight-critical aerial platforms for real-time aerial reconnaissance, infrastructure inspection, and autonomous mapping with fully onboard AI processing.
- AIoT Gateway & Edge Inference Nodes — Cloud-native container support and GbE networking make the Xavier NX an efficient AIoT hub — running inference locally, aggregating sensor data, and selectively pushing processed results to cloud dashboards with minimal bandwidth consumption.
- AI Research & Prototyping — Universities and R&D teams use the Xavier NX as a production-representative edge AI platform — its full CUDA, TensorRT, and JetPack ecosystem mirrors the toolchain used in large-scale deployments, making benchmarks directly transferable to product.
What's in the Box
- 1× NVIDIA Jetson Xavier NX Module (SoM) — your selected configuration (8GB or 16GB)
Note: a carrier board, heatsink, thermal pad, power supply, and any cameras, display adapters, or expansion cards are not included with the module. These accessories are sold separately. A compatible carrier board and appropriate power supply are required before the module can be used.
Frequently Asked Questions
What carrier boards are compatible with the Jetson Xavier NX Module?
The Jetson Xavier NX uses a standard 260-pin SO-DIMM connector and is compatible with any carrier board explicitly designed for the Xavier NX family — including the official NVIDIA Jetson Xavier NX Developer Kit carrier and third-party boards from Connect Tech, Auvidea, Seeed Studio, and Forecr. Critically, the Xavier NX is not electrically compatible with the original Jetson Nano carrier board despite sharing the same physical SO-DIMM form factor — the two platforms use different pin assignments and cannot be interchanged. Always confirm that the carrier board vendor explicitly lists Jetson Xavier NX support before purchasing. Third-party carriers often add ruggedised connectors, multiple GbE ports, M.2 NVMe slots, and extended temperature ratings not found on the reference design.
What power supply does the Jetson Xavier NX require?
Power is delivered through the carrier board, so the exact supply specification is carrier-dependent. The official NVIDIA Xavier NX Developer Kit carrier uses a 9V–20V DC barrel jack (centre-positive, 5.5mm/2.5mm), with a 19V adapter typically recommended to cover the module plus attached peripherals. The module supports three configurable power modes — 10W, 15W, and 20W — and your supply must sustain the peak wattage of your chosen mode plus headroom for cameras, SSDs, and USB devices. For the 20W mode under full AI inference load, a supply rated at 40W or more is advisable to avoid voltage droop and instability. Always consult your specific carrier board's datasheet for exact input voltage range and connector requirements.
Which operating system and JetPack version should I use?
The Jetson Xavier NX officially supports NVIDIA JetPack 4.x and JetPack 5.x, with JetPack 5.1.x being the current long-term supported release for the Xavier series, based on Ubuntu 20.04 LTS. JetPack 5 ships with CUDA 11.4, TensorRT 8.x, cuDNN 8.x, OpenCV, VPI, and the Multimedia API pre-installed and ready to use. Note that JetPack 6 is not supported on Xavier NX — it requires the Orin-generation SoC. NVIDIA's SDK Manager tool on a Linux Ubuntu host handles flashing, firmware updates, and SDK package installation in a guided workflow. For new projects, JetPack 5.1.x is the recommended baseline.
What storage options are available — does it need an SSD to boot?
Unlike the Jetson Orin Nano, the Jetson Xavier NX includes 16GB of onboard eMMC 5.1 storage — the module can boot directly from internal flash without any external SSD or microSD card. This simplifies initial setup and reduces BOM cost for production designs. For applications requiring more capacity or faster I/O, most carrier boards expose an M.2 M-key NVMe SSD slot via PCIe Gen3 — this is preferred for large model storage, datasets, and logging. A 128GB or 256GB NVMe SSD is a common production pairing. The onboard eMMC can simultaneously host the OS while the NVMe SSD is used exclusively for data and AI model storage.
What additional hardware do I need to get started?
At minimum you need: a compatible Xavier NX carrier board, a heatsink with thermal interface material (active cooling recommended for sustained 15W/20W loads), and a power supply matched to your carrier. Because the Xavier NX has onboard eMMC, you do not need to purchase a boot SSD for initial bring-up — though additional storage is recommended for production workloads. For first-time setup, a display (HDMI or DP adapter), USB keyboard, and mouse are useful — though headless SSH setup is also viable after the first flash. A host PC running Ubuntu 20.04 or 22.04 with NVIDIA SDK Manager is required to flash JetPack onto the module for the first time.
How does the Jetson Xavier NX compare to the Jetson Orin NX?
The Jetson Orin NX is the next-generation successor and delivers a significant performance step up — up to 100 TOPS (Orin NX 16GB) versus 21 TOPS on the Xavier NX. The Orin NX uses the newer Ampere GPU architecture (vs. Volta on Xavier NX), supports JetPack 6 on Ubuntu 22.04, and offers higher memory bandwidth via LPDDR5. However, the Jetson Xavier NX remains a strong production-proven platform with a mature software ecosystem, a large installed carrier board base, and critically — hardware video encoding (NVENC) which the Jetson Orin Nano lacks entirely. The Xavier NX is the right choice for projects already aligned to JetPack 5 or requiring the specific 21 TOPS Volta performance envelope with full hardware encode/decode.
How many GPIO and serial interface pins are available?
The Jetson Xavier NX exposes a comprehensive I/O set through its 260-pin SO-DIMM connector, including UART, SPI, I2C, I2S, CAN bus interfaces, GPIO, and PWM outputs. The exact signals accessible to your application depend on your carrier board design — the official NVIDIA Developer Kit routes a subset to a 40-pin expansion header with a layout broadly familiar to Raspberry Pi users. The CAN bus interface is particularly valuable for robotics and automotive applications requiring deterministic communication with motor controllers and sensor arrays. For custom carrier board designs, NVIDIA's Xavier NX pinmux spreadsheet fully documents every signal assignment and alternate function available on the SO-DIMM connector.
Is this module suitable for beginners, or is it aimed at professionals?
The standalone Jetson Xavier NX Module (SoM) is primarily aimed at embedded engineers, OEM product designers, and experienced AI developers building custom hardware around the module. If you are new to Jetson and want to experiment first, the Jetson Xavier NX Developer Kit — which bundles a carrier board — is the recommended starting point. Once comfortable with JetPack, TensorRT model deployment, and system bring-up workflows, the standalone module is the correct choice for custom carrier board designs and production scaling. NVIDIA's "Hello AI World" tutorial series and the active Jetson Developer Forum community substantially lower the learning curve regardless of prior Jetson experience.
What is the most common mistake users make when setting up the Xavier NX?
The most frequent mistake is attempting to use a Jetson Nano carrier board with the Xavier NX module — despite the same physical SO-DIMM form factor, the two platforms are electrically incompatible and the module will not function correctly on a Nano carrier. A second common error is selecting a power supply sized only for the module's nominal wattage without accounting for attached peripherals — under-powered supplies cause random reboots and instability under AI inference load. Finally, many users overlook that initial JetPack flashing requires a Linux Ubuntu host PC running NVIDIA SDK Manager — this step cannot be performed from a Windows machine directly, so plan your bring-up environment before the hardware arrives.
Where can I find official documentation, firmware updates, and community support?
All official documentation, hardware design guides, datasheets, pinmux tools, and JetPack firmware are hosted on the NVIDIA Jetson Developer Zone at developer.nvidia.com/embedded. NVIDIA's SDK Manager tool manages JetPack flashing and software updates from a Ubuntu host machine. Community support, model optimisation discussions, hardware bring-up guidance, and carrier board recommendations are available on the NVIDIA Developer Forums — Jetson & Embedded Systems section, actively monitored by NVIDIA engineers and the wider Jetson community. For AI deployment tutorials, NVIDIA's "Hello AI World" and Jetson AI Fundamentals courses are specifically designed around the Jetson Xavier hardware platform and are the recommended starting point for new users.
