NVIDIA Jetson Orin Nano Super Developer Kit
NVIDIA Jetson Orin Nano Super Developer Kit — 67 TOPS AI Performance — 1024-Core Ampere GPU — 102 GB/s LPDDR5 The Jetson Orin Nano Super Developer Kit is the most...
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NVIDIA Jetson Orin Nano Super Developer Kit — 67 TOPS AI Performance — 1024-Core Ampere GPU — 102 GB/s LPDDR5
The Jetson Orin Nano Super Developer Kit is the most capable compact edge AI computer NVIDIA has released at this power envelope — delivering 67 TOPS of INT8 AI performance inside a 7W–25W footprint that fits robots, drones, and field-deployed systems alike. Backed by the full JetPack 6.2 SDK with CUDA 12.6, TensorRT 10.3, cuDNN 9.3, and DLA 3.1, it provides every tool needed to develop and ship production AI models without the cloud.
Key Highlights
- 67 TOPS On-Device AI — No Cloud Required — Run complex neural networks — object detection, segmentation, LLMs, and NLP — entirely on-device at 67 INT8 TOPS sparse, eliminating cloud latency and recurring inference costs in a single board.
- 1024-Core Ampere GPU @ 1020 MHz — 32 Tensor Cores handle mixed-precision inference and training loops at a GPU clock 60% higher than the original Orin Nano, delivering smoother real-time pipelines without stepping up to a larger board.
- 102 GB/s Memory Bandwidth — 50% Wider Than Before — The shared 8GB LPDDR5 pool feeds the CPU, GPU, and DLA simultaneously without bottlenecking multi-model pipelines, enabling richer sensor-fusion architectures on a single compact board.
- Flexible 7W – 25W Super Power Mode — The new 25W Super mode unlocks peak throughput for demanding workloads, while 7W and 15W modes extend runtime in battery-powered or thermally constrained field deployments — all switchable at runtime with no hardware changes.
- Full JetPack 6.2 SDK — Pre-Integrated & Tested — Ships with CUDA 12.6, TensorRT 10.3, cuDNN 9.3, VPI 3.2, and DLA 3.1 on Ubuntu 22.04 — every component in the NVIDIA AI stack verified together so first-day productivity requires zero manual dependency resolution.
- Dual 40-Pin GPIO Headers for Sensor Fusion — Two industry-standard expansion headers expose UART, SPI, I2C, I2S, and PWM at 3.3V logic levels, enabling multi-sensor fusion, actuator control, and custom peripheral integration without a separate microcontroller.
- Six USB 3.2 Gen 2 Type-A Ports at 10 Gbps Each — Connect depth cameras, USB hubs, storage drives, and lab instruments simultaneously without bandwidth contention — a port density rarely found on embedded AI boards at this form factor and power class.
- NVMe SSD Expansion via M.2 Key M — The M.2 Key M socket accepts NVMe SSDs up to 2280 size over PCIe 3.0 ×4, delivering several times the sustained I/O throughput of microSD for dataset-heavy training, logging, and high-throughput inference pipelines.
- Native ROS 2 Humble & Isaac ROS Support — Hardware-accelerated computer vision via VPI and the Isaac ROS Gem library plug directly into the ROS 2 Humble graph, making this the preferred development target for the edge robotics ecosystem.
Technical Specifications
| Specification | Details |
| AI Performance | 67 TOPS (INT8 Sparse) / 33 TOPS (INT8 Dense) |
| GPU | 1024-core NVIDIA Ampere with 32 Tensor Cores @ 1020 MHz |
| CPU | 6-core Arm Cortex-A78AE v8.2 64-bit @ 1.7 GHz, 1.5MB L2 + 4MB L3 |
| Memory | 8GB 128-bit LPDDR5 @ 102 GB/s |
| Storage | microSD slot + M.2 Key M (NVMe SSD, PCIe 3.0 ×4, up to 2280) |
| USB | 6× USB 3.2 Gen2 Type-A (10 Gbps) + 1× USB 3.2 Type-C (Host / Device / Recovery) |
| Display | 1× DisplayPort 1.2 |
| Networking | Gigabit Ethernet |
| Wireless | M.2 Key E Wi-Fi / Bluetooth (pre-installed) |
| Camera | 2× MIPI CSI-2 connectors (up to 4-lane) |
| GPIO & Expansion | 2× 40-pin headers (UART, SPI, I2C, I2S, PWM) |
| Power | 7W – 25W (configurable power modes) |
| Software Stack | JetPack 6.2+, Ubuntu 22.04, CUDA 12.6, TensorRT 10.3, cuDNN 9.3, VPI 3.2, DLA 3.1 |
Common Applications & Use Cases
- Autonomous Mobile Robots — Fuse lidar, depth camera, and IMU data while running concurrent SLAM and path-planning models on a single board compact enough to fit inside a tabletop mobile chassis.
- Smart Camera & Vision Systems — Deploy multi-stream object detection, tracking, and licence-plate recognition at the camera edge, keeping raw video on-premises and eliminating bandwidth costs to the cloud entirely.
- Drone & UAV Payload Processing — Perform real-time aerial image segmentation and obstacle-avoidance inference onboard without ground-station uplinks or cellular data, enabling fully autonomous flight in GPS-denied environments.
- Industrial Automation & Quality Inspection — Run visual defect-detection models directly on the factory floor and trigger actuators or alerts via GPIO with sub-millisecond latency, without routing sensitive video to a centralised server.
- Edge NLP & Voice Interfaces — Process wake-word detection, speech-to-text, and intent classification locally — ideal for industrial HMIs, kiosks, and assistive devices with strict data-privacy or offline-operation requirements.
- Medical & Biomedical Imaging — Accelerate portable diagnostic devices that analyse X-ray, ultrasound, or endoscopic imagery with TensorRT-optimised CNNs while keeping patient data completely off external networks.
- Smart Agriculture & Field Robotics — Mount on autonomous tractors or sprayers to run crop-disease detection and yield-estimation models in environments with no reliable cellular coverage or cloud uplink.
- Retail & Logistics Analytics — Analyse foot traffic, shelf occupancy, and customer behaviour in real time at the store edge, processing sensitive video locally and delivering insights without raw data leaving the premises.
- Educational AI & Robotics Research — A full CUDA + TensorRT environment on a low-power board makes it ideal for university labs, capstone projects, and competitions such as FIRST Robotics and Robomaster.
- ROS 2 & Isaac ROS Development — Native ROS 2 Humble support, hardware-accelerated VPI, and the Isaac ROS Gem library make this the go-to development target for building production robotics pipelines on the ROS 2 edge ecosystem.
What's in the Box
- Jetson Orin Nano 8GB Super module (pre-installed on reference carrier board)
- Active heatsink with cooling fan (pre-attached)
- 19V / 45W power supply
- Power cable — Type B (US / JP)
- Power cable — Type I (CN)
- Quick Start & Support Guide
Note: accessories such as microSD cards, NVMe SSDs, USB keyboards, displays, cameras, and cables are sold separately and not included unless stated above.
Frequently Asked Questions
What operating systems and software does the Jetson Orin Nano Super Developer Kit support?
The kit runs NVIDIA JetPack 6.2+, built on Ubuntu 22.04 LTS with Linux kernel 5.15. The software stack includes CUDA 12.6, TensorRT 10.3, cuDNN 9.3, VPI 3.2, DLA 3.1, and the DeepStream SDK — everything needed for end-to-end AI pipeline development. Popular frameworks including PyTorch, TensorFlow, and ONNX Runtime are available as optimised containers from the NVIDIA NGC registry. ROS 2 Humble is fully supported alongside the Isaac ROS hardware-accelerated Gem library. The platform also supports Triton Inference Server for multi-model serving at the edge.
What power supply does the Jetson Orin Nano Super Developer Kit require?
The kit requires a 19V DC barrel-jack input through the included supply, rated at 45W — enough headroom for the full 25W Super power mode plus USB peripheral draw. Do not substitute a generic USB-C PD adapter or similarly rated laptop charger; voltage regulation on Jetson boards is strict and an incorrect supply will cause random reboots or prevent boot entirely. The barrel connector is a standard 5.5mm / 2.5mm centre-positive type — any replacement supply must match this specification exactly. The included supply accepts 100–240V AC input, making it globally compatible, though a local plug adapter may be needed outside the US, JP, or CN regions. Always power the board from the barrel jack rather than the USB Type-C port for stable operation under full load.
Can I upgrade the firmware, and how does JetPack updating work?
JetPack is updated via NVIDIA SDK Manager on an Ubuntu host PC, or by reflashing the microSD card or NVMe drive with a fresh image from the Jetson Software Downloads page. The "Super" performance boost is a software-level unlock delivered with JetPack 6.2 — meaning owners of the original Jetson Orin Nano 8GB Developer Kit can reach Super performance figures through a firmware upgrade alone, with no new hardware required. Always back up your work before reflashing, as the process overwrites the root filesystem entirely. Incremental apt-based updates can apply minor security patches between major JetPack releases without a full reflash. NVIDIA typically maintains two active JetPack branches at a time, with release notes published on the JetPack SDK page at developer.nvidia.com.
What storage options are available, and which should I use?
The carrier board provides a microSD card slot and an M.2 Key M socket for NVMe SSDs in 2230 or 2280 form factor over PCIe 3.0 ×4. For early prototyping, a UHS-I Class 10 / A2-rated microSD card at 64GB or larger is sufficient to boot JetPack and run light workloads. For production, dataset-heavy, or logging-intensive applications, an NVMe SSD is strongly recommended — it delivers several times the sequential throughput and far superior sustained I/O performance, as microSD throttles significantly under prolonged write loads. USB storage is supported but is not suitable as the primary root device due to latency and bus-sharing limitations. For best overall performance, boot from NVMe and reserve the microSD slot as a secondary or recovery medium.
What accessories do I need to get started?
Beyond what's in the box, you'll need a microSD card (64GB+ recommended) or an NVMe SSD for the OS, a USB keyboard and mouse, a DisplayPort monitor or a DisplayPort-to-HDMI active adapter, and a USB-A to USB-C cable for initial flashing via recovery mode. A host PC running Ubuntu 20.04 or 22.04 is required for SDK Manager-based firmware flashing — though pre-built microSD images are available for a simpler first-boot experience without the host PC requirement. For camera-based projects, MIPI CSI-2 sensor modules compatible with the Jetson Orin Nano (such as the IMX219 or IMX477 class) connect directly to the two on-board CSI connectors. An NVMe SSD is optional but strongly recommended for any dataset storage or AI training workload. All accessories are sold separately and are not included with the kit.
How does the Orin Nano Super compare to the original Jetson Orin Nano?
The Super achieves 67 TOPS versus the original's 40 TOPS — a 67% improvement — driven by a GPU clock increase from 635 MHz to 1020 MHz and a memory bandwidth uplift from 68 GB/s to 102 GB/s. A new 25W Super power mode provides additional thermal headroom not available on the original, which topped out at 15W. The physical module and carrier board are pin-compatible, and the Super performance figures are available to original 8GB Orin Nano hardware owners through a JetPack 6.2 firmware upgrade — no hardware swap required. The improvement is particularly impactful for generative AI workloads such as LLMs, VLMs, and Vision Transformers, which are memory-bandwidth-bound and benefit nearly linearly from the 50% bandwidth gain. There are no changes to the CPU core count, I/O complement, or board form factor.
How many GPIO pins are available, and what interfaces do they expose?
The developer kit provides two 40-pin GPIO expansion headers exposing UART, SPI, I2C, I2S, PWM, and general-purpose digital I/O at 3.3V logic levels. The pinout is fully documented in the official Jetson Orin Nano Developer Kit User Guide and is partially compatible with Raspberry Pi HATs. Additionally, the board provides 2× MIPI CSI-2 camera connectors (up to 4-lane each), six USB 3.2 Gen 2 Type-A ports, one USB Type-C, Gigabit Ethernet, DisplayPort 1.2, and a pre-installed M.2 Key E Wi-Fi/Bluetooth module. The MIPI CSI-2 connectors support sensors up to the IMX477 12MP class, making them suitable for stereo vision, depth, and hyperspectral imaging rigs. GPIO control in Python is handled via the Jetson.GPIO library, which follows a convention similar to RPi.GPIO for straightforward migration from Raspberry Pi projects.
Is this kit suitable for beginners, or is it aimed at advanced developers?
The Jetson Orin Nano Super Developer Kit caters to both audiences effectively. Beginners can follow NVIDIA's Hello AI World tutorial series to run pre-trained object-detection and image-classification models within minutes using Python and the jetson-inference library — no prior C++ or embedded Linux experience required. Advanced developers have full access to CUDA kernel development, TensorRT custom plugin authoring, the DeepStream SDK for multi-stream video analytics, and direct hardware register access for custom MIPI and GPIO peripherals. The NGC container registry provides optimised baseline containers for PyTorch, TensorFlow, and Triton that eliminate the most time-consuming environment setup steps. The active NVIDIA Developer Forums and the JetsonHacks community offer fast resolutions to common issues at any experience level.
What's a common mistake to avoid when setting up the Jetson Orin Nano Super?
The most frequent setup mistake is using an underpowered or non-compliant power supply — the board requires a stable 19V source, and generic USB-C PD adapters or unregulated laptop chargers will cause random reboots or fail to boot under load. A second common issue is running MAXN (25W Super) mode without adequate airflow — the pre-attached fan must be connected and unobstructed to prevent thermal throttling under sustained AI workloads. A third pitfall is flashing an older JetPack 5.x image instead of JetPack 6.x, which does not include the Super power-mode unlock and will silently cap performance at the original 40-TOPS profile. Always verify the active power mode with sudo nvpmodel -q and monitor thermals live with tegrastats. Confirm the power mode is correct after every reflash, as it does not persist across full image reinstalls.
Where can I find official documentation, firmware updates, and community support?
Official documentation lives at the NVIDIA Jetson Developer Portal (developer.nvidia.com/embedded), including the Orin Nano Developer Kit User Guide, full pinout diagrams, and JetPack release notes. Firmware and SDK downloads are managed through NVIDIA SDK Manager or the Jetson Software Downloads page at developer.nvidia.com/embedded/downloads. For community support, the NVIDIA Developer Forums (Jetson & Embedded Systems category) are the most active resource, with NVIDIA engineers regularly participating in technical threads. The JetsonHacks blog and associated GitHub repositories provide community-maintained tutorials for hardware add-ons, custom carrier boards, and popular AI framework integrations. The NVIDIA NGC catalogue at ngc.nvidia.com hosts official pre-built containers for PyTorch, TensorFlow, Triton, and DeepStream that are tested and validated against each JetPack release.
