NVIDIA Jetson Orin Nano 8GB Developer Kit

The NVIDIA Jetson Orin Nano Developer Kit sets a new standard for creating entry-level AI-powered robots, smart drones, and intelligent vision systems, as NVIDIA announced at NVIDIA GTC 2023. It also simplifies getting started with the NVIDIA Jetson Orin Nano series. Compact design, numerous connectors, and up to 40 TOPS of AI performance make this developer kit ideal for transforming your visionary concepts into reality. 

The developer kit consists of a Jetson Orin Nano 8 GB module and a reference carrier board that can accommodate all NVIDIA Jetson Orin Nano and NVIDIA Jetson Orin NX modules, providing an ideal platform for prototyping next-generation edge AI products. 

The Jetson Orin Nano 8 GB module features an NVIDIA Ampere architecture GPU with 1024 CUDA cores, 32 third-generation Tensor Cores, and a 6-core Arm CPU, enabling multiple concurrent AI application pipelines and high-performance inference. The developer kit carrier board boasts a wide array of connectors, including two MIPI CSI connectors supporting camera modules with up to four lanes, enabling higher resolution and frame rates than before.

The prior-generation Jetson Nano Developer Kit made AI accessible to everyone. The new Jetson Orin Nano Developer Kit raises the bar for entry-level AI development with 80x the performance, enabling developers to run any kind of modern AI models, including transformer and advanced robotics models. Not only does it provide a huge boost in AI performance over the prior-generation Jetson Nano, Jetson Orin Nano also provides 5.4x the CUDA compute, 6.6x the CPU performance, and 50x the performance per watt.

Figure 1. Performance and efficiency comparison between NVIDIA Jetson Orin Nano and NVIDIA Jetson Nano

NVIDIA Jetson Orin Nano Developer Kit features

The NVIDIA Jetson Orin Nano Developer Kit includes a special NVIDIA Orin Nano 8 GB module with an SD-card slot, a reference carrier board, preassembled heatsink/fan, a 19 V DC power supply, and an M.2-Key E based wireless networking module. In addition to the bootable microSD card slot, two M.2 Key-M NVMe sockets are provided on the underside of the carrier for high-speed storage.

Figure 2. The Jetson Orin Nano Developer Kit

Jetson Orin Nano runs all modern AI models

The Jetson Orin Nano Developer Kit, with up to 40 TOPS of AI performance, can run all modern AI models. This major leap in compute makes the most demanding AI applications possible, including running transformer models right at the edge, which was not possible before with Jetson Nano.

Transformer models are the basis of recent generative AI applications like ChatGPT and DALL-E, which are taking the world by storm. A transformer model learns context and meaning by tracking the relationship between elements in sequential data, eliminating the need for a large labeled dataset.

Get started today with support for:

• City Segmentation for segmenting urban cityscapes into different classes
• PeopleNet Transformer based on the Deformable Detection Transformer
• BERT for NLP

Jetson software accelerates AI and TTM

Jetson Orin Nano Developer Kit runs the NVIDIA AI software stack, with available use-case-specific application frameworks. These include NVIDIA Isaac for robotics, NVIDIA DeepStream for vision AI, and NVIDIA Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic datageneration (SDG), and with NVIDIA TAO Toolkit for fine-tuning pretrained AI models from the NGC catalog.

Figure 3 shows results from running some computer vision benchmarks with Jetson Orin Nano using the upcoming NVIDIA JetPack 5.1.1. These results show that the developer kit raises the bar for entry-level computer vision. Testing included some dense INT8 and FP16 pretrained models from NGC, and an Industry Resnet-50 Benchmark. The benchmark testing included the following:

• NVIDIA PeopleNet v2.5 for the highest accuracy people detection
• NVIDIA ActionRecognitionNet 2D and 3D models
• NVIDIA LPRNet for license plate recognition
• NVIDIA DashCamNet, BodyPoseNet for multiperson human pose estimation
• ResNet-50 (224×224) model for object detectio

Figure 3. Results from benchmark testing with pretrained models comparing the Jetson Nano to the Jetson Orin Nano 8 GB

The Jetson Orin Nano Developer Kit is a versatile platform that supports models trained with NVIDIA TAO Toolkit 4.0, and will soon support newly announced models in TAO Toolkit 5.0. With TAO Toolkit 5.0, developers can take advantage of several state-of-the-art vision transformer models for image classification, object detection, and segmentation use cases. To learn more, see Access the Latest in Vision AI Model Development Workflows with NVIDIA TAO Toolkit 5.0.

NVIDIA Jetson Orin Nano and NVIDIA DeepStream make an ideal combination for edge applications, such as smart retail, smart city intersections, and industrial automation. With the upcoming version of DeepStream, and the Introduction of the GXF runtime, Jetson Orin Nano is an ideal platform for running AI graphs that require tight integration with deterministic systems, common in factory automation use cases.

Additionally, you can quickly familiarize yourself with DeepStream by building applications using the latest version of DeepStream Graph Composer, and deploy them to a Jetson Orin Nano with the click of a button.

Figure 4. Build applications in DeepStream Graph Composer and deploy them to Jetson Orin Nano

Accelerate robotics applications with NVIDIA Isaac on NVIDIA Jetson Orin

NVIDIA Isaac robotics platform is a powerful, end-to-end platform for the development, simulation, and deployment of AI-enabled robots. Specifically NVIDIA Isaac ROS, a collection of hardware-accelerated packages, makes it easier for ROS 2 developers to build high-performance solutions on the Jetson Orin Nano Developer Kit. The new NVIDIA Isaac ROS DP release optimizes ROS 2 nodes processing pipelines, which can be executed on the Jetson Orin Platform. It also provides new DNN-based GEMS designed to increase throughput.

Figure 4 shows the results of running these robotics packages on the Jetson Orin Nano using the upcoming NVIDIA Isaac ROS DP3 release. The performance is measured under load including message transport costs in RCL for practical benchmarking indicative of real-world performance. Testing included the algorithms seen in the chart (Figure 4), including:

• Visual SLAM that enables a robot to compute its location and movement from images by tracking visual features around its environment
• April Tags for AprilTag detection and pose estimation
• Image Detection 
• Image Segmentation
• Proximity Segmentation to determine whether an obstacle is within a proximity field and to avoid collisions with obstacles during navigation
• Stereo Disparity for taking stereo input images and generating a disparity map of the input image for robot navigation

Figure 5. Performance, latency, and resolution of NVIDIA Isaac ROS GEMs on NVIDIA Jetson Orin Nano 8 GB

Applications

• AI-powered robots
• Smart drones
• Intelligent vision system

Specification

• GPU: NVIDIA Ampere architecture with 1024 NVIDIA CUDA Cores and 32 Tensor Cores
• CPU: 6-core Arm Cortex-A78AE v8.2 64-bit CPU 1.5 MB L2 + 4 MB L3
• Memory: 8 GB 128-bit LPDDR5 68 GB/s
• Storage: External through microSD slot, External NVMe through M.2 Key M
• Power: 7 W to 15 W
• Camera: 2x MIPI CSI-2 22-pin Camera Connectors
• M.2 Key M: x4 PCIe Gen3
• M.2 Key M: x2 PCIe Gen3
• M.2 Key E: PCIe (x1), USB 2.0, UART, I2S, and I2C
• USB:Type A: 4x USB 3.2 Gen2 Type C: 1x for Debug and Device Mode
• Networking: 1x GbE Connector
• Display: DisplayPort 1.2 (+MST)
• microSD slot: UHS-1 cards up to SDR104 mode
• Others: 40-pin Expansion Header (UART, SPI, I2S, I2C, GPIO)12-pin button header4-pin fan header DC power jack
• Dimensions: 100 mm x 79 mm x 21 mm(Height includes feet, carrier board, module, and thermal solution)

Documents

• Getting Started Guide
• User Guide
• JetPack SDK
• FAQ

Parts List

• NVIDIA Jetson Orin nano 8GB Developer Kit x1