NVIDIA Jetson Orin NX Module
NVIDIA Jetson Orin NX 8GB — 70 TOPS Edge AI — 1024-Core Ampere GPU — Compact SO-DIMM Module The NVIDIA Jetson Orin NX 8GB delivers up to 70 TOPS of...
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NVIDIA Jetson Orin NX 8GB — 70 TOPS Edge AI — 1024-Core Ampere GPU — Compact SO-DIMM Module
The NVIDIA Jetson Orin NX 8GB delivers up to 70 TOPS of INT8 AI performance — scaling to 117 TOPS in MAXN_SUPER mode with JetPack 6.2 — within the ultra-compact 69.6×45mm SO-DIMM form factor. It combines a 1024-core NVIDIA Ampere GPU, 6-core Arm Cortex-A78AE CPU, dedicated NVDLA v2.0 deep learning acceleration, and hardware video encode/decode into a configurable 10W–40W thermal envelope, making it the go-to compute module for drones, autonomous robots, smart cameras, and portable industrial AI systems. Available alongside the Orin NX 16GB for deployments demanding greater memory capacity and additional CPU cores.
Key Highlights
- 70 TOPS AI Performance — 117 TOPS in MAXN_SUPER Mode — delivers real-time deep learning inference for vision, NLP, and multi-sensor fusion pipelines; JetPack 6.2 unlocks MAXN_SUPER for a 67% throughput boost without a hardware change.
- 1024-Core NVIDIA Ampere GPU with 32 Tensor Cores — mixed-precision matrix operations (FP32, FP16, INT8, INT4) accelerate neural network layers at sub-millisecond latency, enabling concurrent multi-model inference on a single module.
- 1× NVDLA v2.0 Deep Learning Accelerator — offloads steady-state inference from the GPU at up to 20 TOPS, freeing Ampere cores for pre/post-processing, sensor fusion, and secondary model workloads running in parallel.
- 6-Core Arm Cortex-A78AE CPU at 2 GHz — automotive-grade 64-bit cores with hardware ECC handle ROS2 node execution, sensor preprocessing, and Linux OS management without thermal throttling under sustained workloads.
- 8GB 128-Bit LPDDR5 at 102.4 GB/s — wide memory bandwidth prevents bottlenecks when streaming multiple high-resolution camera feeds or running concurrent multi-model inference — double the bandwidth of the previous Xavier NX generation.
- Hardware Video Encode & Decode — a dedicated VPU handles 4K60 H.265 encoding and 8K30 H.265 decoding entirely in hardware, preserving CPU and GPU resources for AI inference tasks running simultaneously.
- Up to 4 MIPI CSI-2 Cameras (8 via Virtual Channels) — 8 D-PHY 1.2 lanes at 20 Gbps aggregate support stereo depth rigs, 360° vision arrays, and simultaneous RGB/thermal capture without an external frame grabber.
- PCIe Gen 4 Connectivity (1×4 + 3×1 lanes) — supports high-throughput NVMe SSDs, FPGA accelerators, and multi-port networking cards directly on the module bus, with substantially lower latency than USB-attached peripherals.
- Configurable TDP: 10W to 40W — four selectable power modes let you tune performance against battery life or thermal budget at deployment time, with no firmware reflash required when switching between modes.
- Xavier NX Carrier Board Compatible — the 260-pin SO-DIMM interface is pin-compatible with Jetson Xavier NX carrier boards, minimising redesign effort and bill-of-materials changes when migrating existing platforms to Orin-class performance.
Technical Specifications
| Specification | Details |
| AI Performance | 70 TOPS (INT8) — 117 TOPS (MAXN_SUPER, JetPack 6.2+) |
| GPU | NVIDIA Ampere Architecture — 1024 CUDA Cores, 32 Tensor Cores, 1173 MHz max |
| CPU | 6-Core Arm® Cortex®-A78AE v8.2 64-bit — 2 GHz max — 1.5MB L2 + 4MB L3 |
| Memory | 8GB 128-bit LPDDR5 — 102.4 GB/s bandwidth |
| Storage | No on-module storage — external NVMe SSD via carrier board M.2 slot |
| DL Accelerator | 1× NVDLA v2.0 (up to 20 TOPS) |
| Vision Accelerator | 1× PVA v2.0 |
| Video Encode | 1×4K60 | 3×4K30 | 6×1080p60 | 12×1080p30 (H.265) — H.264, AV1 |
| Video Decode | 1×8K30 | 2×4K60 | 4×4K30 | 9×1080p60 | 18×1080p30 (H.265) — H.264, VP9, AV1 |
| CSI Camera | Up to 4 cameras (8 via virtual channels) — 8 MIPI CSI-2 lanes — D-PHY 1.2 (up to 20 Gbps) |
| Display | 1× 8K30 multi-mode DP 1.4a (+MST) / eDP 1.4a / HDMI 2.1 |
| PCIe | 1×4 + 3×1 (PCIe Gen 4, Root Port & Endpoint) |
| USB | 3× USB 3.2 Gen2 (10 Gbps) — 3× USB 2.0 |
| Networking | 1× Gigabit Ethernet |
| Other I/O | 3× UART, 2× SPI, 2× I2S, 4× I2C, 1× CAN, DMIC & DSPK, PWM, GPIOs |
| Power | 10W / 15W / 25W / 40W (MAXN_SUPER, JetPack 6.2+) |
| Mechanical | 69.6mm × 45mm — 260-pin SO-DIMM connector |
Which Jetson Orin NX Is Right for You?
Both the 8GB and 16GB share the same Ampere GPU, PCIe Gen 4 connectivity, and SO-DIMM form factor — the key differentiators are memory capacity, CPU core count, and peak AI throughput. Choose the 8GB for compact single-model deployments and power-sensitive platforms; choose the 16GB when running concurrent models, higher-resolution pipelines, or multi-agent robotic stacks that demand larger working memory.
| Feature | Orin NX 8GB | Orin NX 16GB |
| AI Performance | 70 TOPS (117 TOPS MAXN_SUPER) | 100 TOPS (157 TOPS MAXN_SUPER) |
| GPU | 1024 CUDA Cores, 32 Tensor Cores | 1024 CUDA Cores, 32 Tensor Cores |
| CPU | 6-Core A78AE, 2 GHz | 8-Core A78AE, 2 GHz |
| Memory | 8GB LPDDR5 — 102.4 GB/s | 16GB LPDDR5 — 102.4 GB/s |
| DL Accelerator | 1× NVDLA v2.0 | 2× NVDLA v2.0 |
| Power Modes | 10W / 15W / 25W / 40W | 10W / 15W / 25W / 40W |
| Best For | Drones, handheld systems, single-model inference, battery-powered platforms | Multi-model pipelines, robotic arms, high-res video AI, concurrent sensor fusion stacks |
Common Applications & Use Cases
- Autonomous Drones & UAVs — the 10W–25W power envelope and compact SO-DIMM form factor fit directly into UAV flight computers, enabling onboard obstacle avoidance, target tracking, and real-time path planning without a tethered compute unit.
- Industrial Robot Vision — runs real-time object detection, grasp pose estimation, and defect classification simultaneously, with hardware CSI-2 camera support for synchronised multi-camera bin-picking and assembly verification rigs.
- Automated Optical Inspection (AOI) — high-bandwidth LPDDR5 and hardware video decode handle line-scan and area-scan camera feeds at production-line speeds for PCB, semiconductor, and pharmaceutical surface inspection.
- Collaborative Robots (Cobots) — runs ROS2 navigation stacks, sensor fusion middleware, and safety watchdog processes concurrently on dedicated CPU cores while the GPU handles visual odometry and real-time scene understanding.
- Smart Traffic & Surveillance Systems — hardware H.265 encode/decode and DeepStream SDK support multi-stream vehicle tracking, licence plate recognition, and crowd analytics entirely at the edge without cloud connectivity or data egress costs.
- Handheld Medical Imaging Devices — configurable TDP and compact form factor enable battery-powered ultrasound, retinal scanning, and dermatology AI tools that require real-time inference without transmitting patient data to the cloud.
- Edge Inference Servers — TensorRT-optimised INT8 models with NVDLA offloading make the Orin NX 8GB a capable low-power inference server for smart factory edge nodes, retail AI kiosks, and digital signage analytics platforms.
- Natural Language Processing at the Edge — 8GB LPDDR5 is sufficient to run quantised large language models and speech recognition pipelines for voice-controlled industrial HMIs and autonomous service robot interfaces.
- Autonomous Ground Vehicles (AGVs) — paired with a carrier board providing CAN bus, UART, and GPIO breakouts, the Orin NX 8GB integrates directly into AGV motor controllers, LiDAR processing pipelines, and fleet management stacks.
- Research & Academic Prototyping — the SO-DIMM carrier board ecosystem, JetPack SDK, and NVIDIA NGC model catalogue provide a well-documented, production-representative platform for robotics labs and universities developing next-generation embodied AI systems.
What's in the Box
- 1× NVIDIA Jetson Orin NX 8GB Module (with integrated Thermal Transfer Plate)
Note: a carrier board, heatsink, thermal pad, power supply, NVMe SSD, cables, cameras, and display adapters are sold separately and not included with the module. 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 Orin NX 8GB?
The Jetson Orin NX 8GB uses a standard 260-pin SO-DIMM connector that is pin-compatible with carrier boards designed for the Jetson Xavier NX family, allowing most existing carrier board designs to be reused with a JetPack firmware update. Third-party carriers from Connect Tech, Seeed Studio, Auvidea, and Forecr also explicitly support the Orin NX module. Always verify the carrier board vendor lists Orin NX compatibility, since the older Jetson Nano carrier boards use an incompatible connector and are not interchangeable. A carrier board with an M.2 Key M slot is strongly recommended, as it is required for NVMe SSD storage which serves as the primary boot and OS medium.
What power supply does the Jetson Orin NX 8GB require?
Power is delivered entirely through the 260-pin SO-DIMM carrier board connector — there is no separate power input on the module. The carrier board regulates the supply; most commercial designs accept 9V–20V DC input, with a 19V / 65W adapter commonly used to cover all peripherals. Total system draw in standard modes ranges from 10W to 25W; enabling MAXN_SUPER mode with JetPack 6.2 can push peak draw to 40W. For battery-powered designs, budget at least 20W headroom above the chosen TDP to handle transient GPU and CPU burst peaks.
Which operating systems and software frameworks are supported?
The Orin NX 8GB runs Ubuntu-based Linux via the NVIDIA JetPack SDK, which bundles CUDA, cuDNN, TensorRT, DeepStream, and VPI in a tested, unified software stack. Both JetPack 5.x (Ubuntu 20.04) and JetPack 6.x (Ubuntu 22.04 LTS) are supported, with JetPack 6.2 adding MAXN_SUPER performance mode. PyTorch, TensorFlow, and ONNX Runtime are available via JetPack-aligned wheel packages, and ROS2 Humble and Jazzy integrate natively. Docker containers are supported for isolated pipeline deployments, and NVIDIA's NGC catalogue provides hundreds of pre-optimised models ready for TensorRT deployment.
Does the Jetson Orin NX 8GB have built-in storage, and what options are available?
The Orin NX 8GB has no on-module flash or eMMC — all OS and application storage is provided externally through the carrier board. The recommended option is an NVMe SSD via M.2 Key M (PCIe Gen 4), which delivers the best sustained read/write performance for AI workloads, logging-heavy pipelines, and dataset storage. Some carrier boards expose a microSD slot as an alternative, but SD card I/O will bottleneck demanding workloads. A capacity of at least 64GB is recommended to accommodate JetPack, AI models, application code, and log files comfortably.
What accessories are required to get started with the Jetson Orin NX 8GB?
At minimum you need a compatible carrier board, an NVMe SSD for storage, and a DC power supply matched to the carrier's input specification. A display cable (HDMI 2.1 or DisplayPort 1.4a), USB keyboard, and mouse are recommended for initial setup, along with a host PC running NVIDIA SDK Manager on Ubuntu to flash JetPack. A thermal solution — heatsink with thermal interface material and, ideally, an active cooling fan for sustained loads above 15W — is essential; the module includes a Thermal Transfer Plate but requires external cooling hardware to remain within thermal limits.
How does the Jetson Orin NX 8GB compare to the Jetson Xavier NX?
The Orin NX 8GB delivers approximately 5× the AI throughput of the Xavier NX 8GB — from roughly 21 TOPS to 70 TOPS (117 TOPS MAXN_SUPER) — while maintaining the same SO-DIMM form factor for drop-in carrier board reuse. The Ampere GPU replaces the older Volta architecture, adding INT4 precision and significantly improved Tensor Core efficiency per watt. Memory bandwidth doubles from 51.2 GB/s to 102.4 GB/s thanks to LPDDR5, and PCIe upgrades from Gen 3 to Gen 4, delivering higher peripheral throughput. Power modes are also more granular on the Orin NX, offering 10W, 15W, 25W, and 40W profiles versus the Xavier NX's 10W and 15W options.
What GPIO and serial interfaces are available on the Jetson Orin NX 8GB?
Through a compatible carrier board, the Orin NX 8GB exposes 3× UART, 2× SPI, 2× I2S audio, 4× I2C, 1× CAN bus, DMIC and DSPK digital audio, PWM outputs, and multiple configurable GPIO lines. PCIe provides 1×4 + 3×1 Gen 4 lanes, while USB includes 3× USB 3.2 Gen2 (10 Gbps) and 3× USB 2.0 host ports. The CAN bus interface is particularly valuable for robotics and AGV applications requiring deterministic real-time communication with motor controllers and sensors. The exact signals available depend on your carrier board design — consult the carrier schematic to confirm interface routing before connecting external hardware.
Is the Jetson Orin NX 8GB suitable for beginners or is it an advanced platform?
The standalone Orin NX 8GB SoM is primarily aimed at intermediate-to-advanced developers — embedded engineers, AI developers, and OEM product teams building custom hardware. Initial setup requires flashing JetPack via SDK Manager, configuring carrier board device trees, and managing Linux networking and storage, all of which assume embedded Linux familiarity. Developers new to Jetson should start with the Jetson Orin Nano Developer Kit, which ships as a complete kit. Once your environment is established, the Orin NX integrates smoothly with high-level frameworks like PyTorch, DeepStream, and ROS2, significantly lowering the barrier for AI model deployment and iteration.
What common mistakes should I avoid when deploying the Jetson Orin NX 8GB?
The most common mistake is running at MAXN or MAXN_SUPER power mode without a properly rated thermal solution — the Orin SoC aggressively throttles CPU and GPU clocks when the die exceeds thermal limits, causing unpredictable latency spikes in production that are difficult to diagnose. Always validate thermals under sustained full-load conditions before finalising an enclosure design. A second frequent issue is attempting to flash without a recognised storage device — the module requires a formatted NVMe SSD or compatible storage present on the carrier before SDK Manager can deploy JetPack. Finally, verify your carrier board's JetPack compatibility list before updating firmware, as some board support packages lag behind the latest JetPack release by several weeks.
Where can I find official documentation, firmware, and community support?
Official documentation — including the Orin NX Series datasheet, hardware design guide, and JetPack release notes — is hosted on the NVIDIA Jetson Developer Zone at developer.nvidia.com/embedded. Firmware and the NVIDIA SDK Manager installer are available at developer.nvidia.com/nvidia-sdk-manager. Community support, carrier board integration guides, and TensorRT optimisation discussions are active on the NVIDIA Developer Forums — Jetson & Embedded Systems section, monitored by NVIDIA engineers. Pre-optimised AI models ready for Jetson deployment — covering object detection, segmentation, pose estimation, and NLP — are available through the NVIDIA NGC catalogue at ngc.nvidia.com.