NVIDIA Jetson Orin Nano Module
NVIDIA Jetson Orin Nano Module — Up to 40 TOPS Edge AI — 1024-Core Ampere GPU — CUDA & JetPack 6 Ready The NVIDIA Jetson Orin Nano Module is a...
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NVIDIA Jetson Orin Nano Module — Up to 40 TOPS Edge AI — 1024-Core Ampere GPU — CUDA & JetPack 6 Ready
The NVIDIA Jetson Orin Nano Module is a compact AI System-on-Module (SoM) engineered for real-time edge inferencing across robotics, industrial vision, autonomous machines, and AIoT. Available in 4GB (20 TOPS) and 8GB (40 TOPS) configurations, it connects to any Jetson Orin Nano-compatible carrier board via its standard 260-pin SO-DIMM interface, giving developers, research labs, and OEMs a scalable, power-efficient path from prototype to production deployment.
Key Highlights
- Up to 40 TOPS AI Throughput — The 8GB variant delivers 40 TOPS of sparse INT8 inference, enabling real-time object detection, segmentation, and pose estimation entirely at the edge — no cloud round-trip or connectivity dependency required.
- NVIDIA Ampere GPU Architecture — 1024 CUDA cores and 32 Tensor Cores on the 8GB (512 cores / 16 Tensor Cores on the 4GB) provide hardware-accelerated parallel compute purpose-built for deep learning vision and inference workloads.
- High-Bandwidth LPDDR5 Memory — 8GB of 128-bit LPDDR5 at 68 GB/s sustains simultaneous multi-model and multi-stream AI pipelines; the 4GB variant delivers 64-bit LPDDR5 at 34 GB/s for lighter, single-model workloads.
- 6-Core Arm Cortex-A78AE CPU — A high-reliability 64-bit v8.2 CPU running at up to 1.5 GHz handles Linux OS management, sensor preprocessing, and peripheral control in parallel with GPU inference — without performance contention.
- Multi-Camera Input Pipeline — Up to 4 simultaneous cameras (expandable to 8 via virtual channels) over 8-lane MIPI CSI-2 with D-PHY 2.1 at up to 20 Gbps aggregate bandwidth — essential for surround-view robotics and multi-sensor fusion systems.
- PCIe Gen3 High-Speed Expansion — One x4 plus three x1 PCIe Gen3 lanes allow direct attachment of NVMe SSDs, Wi-Fi 6 adapters, AI accelerator cards, and other high-bandwidth peripherals without USB overhead or bottleneck.
- Full CUDA, TensorRT & JetPack Ecosystem — Native support for CUDA, TensorRT, PyTorch, TensorFlow, and NVIDIA JetPack SDK means existing AI models deploy with minimal porting effort and maximum hardware utilisation out of the box.
- Configurable Power Envelopes — Selectable 7W, 15W, and 25W power modes let you tune performance vs. thermal budget — critical for battery-powered field devices, passively cooled enclosures, or full-performance embedded applications.
- Hardware-Accelerated Video Decode — A dedicated NVDEC engine handles up to 1× 4K60 or 11× 1080p30 H.265 streams concurrently, freeing CPU and GPU resources entirely for AI inference tasks running in parallel.
- Ecosystem-Wide SO-DIMM Compatibility — The 69.6 × 45 mm module with a 260-pin SO-DIMM connector is pin-compatible with a wide range of third-party carrier boards from Connect Tech, Seeed Studio, Auvidea, and others — accelerating custom system integration and time-to-market.
Technical Specifications
| Specification | Orin Nano 4GB | Orin Nano 8GB |
| AI Performance | 20 TOPS | 40 TOPS |
| GPU | 512-core NVIDIA Ampere, 16 Tensor Cores | 1024-core NVIDIA Ampere, 32 Tensor Cores |
| Max GPU Frequency | 1.0 GHz | 1.1 GHz |
| CPU | 6-core Arm Cortex-A78AE v8.2 64-bit, up to 1.5 GHz — 1.5MB L2 + 4MB L3 | |
| Memory | 4GB 64-bit LPDDR5 — 34 GB/s | 8GB 128-bit LPDDR5 — 68 GB/s |
| Storage | External NVMe SSD via M.2 M-key (PCIe Gen3) — no internal eMMC | |
| Camera | Up to 4 cameras (8 via virtual channels) — 8-lane MIPI CSI-2, D-PHY 2.1 up to 20 Gbps | |
| Video Encode | 1080p30 software-based (CPU/FFmpeg) — no dedicated NVENC hardware encoder | |
| Video Decode | 1× 4K60 (H.265) — 2× 4K30 — 5× 1080p60 — 11× 1080p30 (hardware NVDEC) | |
| PCIe | 1×4 + 3×1 PCIe Gen3 (Root Port & Endpoint) | |
| USB | 3× USB 3.2 Gen2 (10 Gbps) + 3× USB 2.0 | |
| Networking | 1× GbE (10/100/1000 Base-T) | |
| Display | 1× 4K30 multi-mode DP 1.2 (+MST) / eDP 1.4 / HDMI 1.4 | |
| Other I/O | 3× UART, 2× SPI, 2× I2S, 4× I2C, 1× CAN, DMIC & DSPK, PWM, GPIOs | |
| Power | 7W – 10W | 7W – 15W |
| Mechanical | 69.6 × 45 mm — 260-pin SO-DIMM connector | |
Which Jetson Orin Nano Is Right for You?
Both variants share the same CPU, I/O set, and connectivity — the decision is purely about memory bandwidth and AI throughput headroom. The 4GB module is the right fit for single-model inference, lightweight NLP tasks, or memory-constrained embedded designs where the lower power ceiling is an asset. The 8GB module is the clear choice whenever you need to run multiple models simultaneously, process 4K multi-stream video analytics, or push sustained throughput above 20 TOPS in production.
| Feature | Orin Nano 4GB | Orin Nano 8GB |
| RAM | 4GB 64-bit LPDDR5 | 8GB 128-bit LPDDR5 |
| Memory Bandwidth | 34 GB/s | 68 GB/s |
| AI Performance | 20 TOPS | 40 TOPS |
| GPU Cores | 512 CUDA cores, 16 Tensor Cores | 1024 CUDA cores, 32 Tensor Cores |
| Power Range | 7W – 10W | 7W – 15W |
| Best For | Single-model inference, power-constrained designs, lightweight NLP & vision tasks | Multi-model pipelines, 4K multi-stream analytics, demanding real-time inference |
Common Applications & Use Cases
- Edge AI Inference — Run YOLO, ResNet, EfficientDet, and transformer-based models entirely on-device, eliminating cloud latency and data privacy risks in real-time decision systems deployed in the field.
- Robotics & AMR / AGV Navigation — Multi-camera input and low-latency Ampere GPU inference make the Orin Nano the ideal compute brain for autonomous mobile robots, warehouse AGVs, and collaborative robot arms requiring real-time environmental perception.
- Industrial Computer Vision & Defect Inspection — Detect surface defects, measure component tolerances, and classify assembly errors on high-speed production lines with sub-millisecond TensorRT-optimised inference — no cloud connectivity required.
- AI-Powered Video Surveillance — Process up to 4 simultaneous camera feeds onboard with person detection, anomaly recognition, and license plate identification — without transmitting raw footage off-site or compromising data privacy.
- Edge NLP & Conversational AI — Deploy compact language models and voice-processing pipelines locally on service kiosks, retail assistants, and point-of-care devices where cloud connectivity is unreliable, costly, or prohibited.
- Medical Imaging & Point-of-Care Diagnostics — Run inference on X-ray, ultrasound, and digital pathology images at the bedside or in remote clinics using a certified hardware-grade module with defined, repeatable power envelopes.
- Retail Analytics & Loss Prevention — Count customers, monitor shelf occupancy, detect unusual behaviours, and generate heatmaps in real time — all processed on-device to comply with GDPR and regional data protection regulations.
- Drone & UAV Payloads — The 7W minimum power mode and compact SoM footprint allow integration into weight-critical UAV payloads for real-time aerial AI, infrastructure inspection, and autonomous mapping missions.
- Smart Agriculture & Precision Farming — Detect plant disease, count crops, and guide autonomous sprayer systems using vision models running on solar or battery-powered field units with no internet dependency.
- AI Research & Education — Universities, research labs, and maker communities use the Jetson Orin Nano as an accessible yet production-representative platform for developing, benchmarking, and publishing edge AI architectures.
What's in the Box
- 1× NVIDIA Jetson Orin Nano Module (SoM) — your selected configuration (4GB or 8GB)
Note: a carrier board, heatsink, thermal pad, power supply, cables, NVMe SSD, and any cameras or display adapters 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 Orin Nano Module?
The Jetson Orin Nano Module uses a standard 260-pin SO-DIMM connector and is compatible with any carrier board designed for the Jetson Orin Nano or Jetson Orin NX family. This includes the official NVIDIA Jetson Orin Nano Developer Kit carrier, plus third-party boards from Connect Tech, Seeed Studio, Auvidea, and Forecr, among others. Always verify that the carrier board vendor explicitly lists support for the Orin Nano module, since the older Jetson Nano carrier boards use an incompatible connector and are not interchangeable. Third-party carriers often add features like multiple Ethernet ports, M.2 expansion, or ruggedised I/O not found on the reference design.
What power supply does the Jetson Orin Nano Module require?
Power is delivered through the carrier board, so the supply specification depends on your carrier design. The official NVIDIA Developer Kit carrier uses a 9V–20V DC barrel jack (centre-positive, 5.5mm/2.5mm), and a 19V / 65W adapter is typically recommended to cover all peripherals. The module itself operates across configurable power modes of 7W, 15W, and 25W — ensure your supply can sustain the peak wattage of your chosen mode plus headroom for attached peripherals such as SSDs, cameras, and USB devices. Third-party carrier boards may specify different input voltage ranges — always consult the carrier datasheet before selecting a power supply.
Which operating systems and JetPack versions are supported?
The Jetson Orin Nano Module officially supports NVIDIA JetPack 5.x and JetPack 6.x. The current production release is JetPack 6.2, based on Ubuntu 22.04 LTS with Linux Kernel 5.15 — it ships with CUDA, TensorRT, cuDNN, OpenCV, VPI, and Multimedia API pre-installed. JetPack 5.x releases are based on Ubuntu 20.04 and remain supported for projects requiring that baseline. Modules shipped with early firmware may require a firmware update before JetPack 6.x can be flashed — NVIDIA's SDK Manager tool handles this process automatically on a Linux host.
What storage options are available — do I need an SD card or eMMC?
Unlike the original Jetson Nano, the Jetson Orin Nano Module has no internal eMMC or onboard flash — external storage is required to boot. The recommended option is an NVMe SSD via M.2 M-key (PCIe Gen3), which is the primary boot medium on the official developer kit and delivers the best sustained read/write performance for AI workloads. Some carrier boards also provide a microSD slot usable for booting or data storage, but NVMe is strongly preferred. Choose an NVMe SSD of at least 32GB; 128GB or larger is recommended to accommodate JetPack, models, datasets, and application logs comfortably.
What additional hardware do I need to get started with this module?
At minimum you will need: a compatible carrier board, an NVMe SSD (for the OS and JetPack), a suitable power supply matched to the carrier, and a heatsink with thermal interface material (active cooling is recommended for sustained loads above 15W). For initial setup, a display (HDMI or DisplayPort adapter), USB keyboard, and mouse are useful — though headless setup over SSH is also possible after the first flash. A host PC running Ubuntu 20.04 or 22.04 with NVIDIA SDK Manager installed is required to flash JetPack onto the module for the first time.
How does the Jetson Orin Nano compare to the original Jetson Nano?
The Jetson Orin Nano is a generational leap beyond the original Jetson Nano (2019). The Orin Nano delivers up to 40× more AI performance, moves from a Maxwell GPU to NVIDIA Ampere architecture, and adds LPDDR5 memory with significantly higher bandwidth. It also gains proper JetPack 5/6 support on Ubuntu 20.04/22.04, while the original Jetson Nano reached end-of-life at JetPack 4.6. Critically, the two platforms are physically and software-incompatible — carrier boards, images, and GPIO libraries are not interchangeable — so migrating requires a full system redesign rather than a simple module swap.
How many GPIO and serial interface pins are available?
The module exposes a rich I/O set through the 260-pin SO-DIMM connector: 3× UART, 2× SPI, 2× I2S, 4× I2C, 1× CAN bus, PWM outputs, and multiple GPIOs — plus digital microphone (DMIC) and digital speaker (DSPK) interfaces for audio. The exact signals available to your application depend on your carrier board design; the official 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 real-time communication with motor controllers and sensors.
Is this module suitable for beginners, or is it an advanced platform?
The standalone Jetson Orin Nano Module (SoM) is primarily targeted at intermediate-to-advanced users — embedded engineers, AI developers, and OEM product teams building custom hardware around the SoM. If you are new to Jetson and want to experiment first, the Jetson Orin Nano Developer Kit (which bundles a carrier board) is the recommended starting point. Once you are comfortable with JetPack, model deployment workflows, and system integration, the standalone module is the correct choice for designing custom carrier boards or scaling into production. NVIDIA's "Hello AI World" and "Jetson AI Fundamentals" tutorials, along with the active Jetson Developer Forum community, substantially lower the learning curve.
Does the Jetson Orin Nano support hardware video encoding?
This is one of the most important things to verify before purchasing: the Jetson Orin Nano does not include a dedicated hardware video encoder (NVENC) — this is a confirmed hardware limitation versus larger Orin variants like the Orin NX and AGX Orin. Video encoding (H.264/H.265 output streams) must be handled in software via CPU cores using FFmpeg or GStreamer, limiting simultaneous encode to approximately 1080p30. If your application requires concurrent hardware encoding of multiple high-resolution streams, the Jetson Orin NX is the appropriate upgrade. Video decode, by contrast, is fully hardware-accelerated via the dedicated NVDEC engine and supports up to 4K60 H.265.
Where can I find official documentation, firmware updates, and community support?
All official documentation, hardware design files, datasheets, and JetPack firmware are hosted on the NVIDIA Jetson Developer Zone at developer.nvidia.com/embedded. NVIDIA's SDK Manager tool handles JetPack flashing and software updates from a Ubuntu host. Community support, model optimisation discussions, and hardware bring-up guidance are available on the NVIDIA Developer Forums — Jetson & Embedded Systems section, which is actively monitored by NVIDIA engineers. For AI deployment tutorials, the freely available "Hello AI World" and "Jetson AI Fundamentals" courses are specifically designed around Jetson Orin Nano hardware and are the recommended starting point for new users.
