NVIDIA Jetson Nano 4GB B01 Developer Kit for Edge AI/ROS - A57 1.43GHz, 128-core Maxwell GPU

Overview

Jetson NANO 4GB B01 AI Large Model Developer Kit is a compact developer kit (developer board platform) designed for getting started with AI. It can run multiple neural networks in parallel for applications such as image classification, object detection, segmentation, and speech processing, and can run in as little as 5 watts.

This Jetson Nano platform uses a quad-core ARM Cortex-A57 processor and a 128-core Maxwell GPU with 4GB LPDDR memory, and supports popular AI frameworks and algorithms such as TensorFlow, PyTorch, Caffe/Caffe2, Keras, and MXNet.

Key Features

• CPU: Quad-core ARM A57 @ 1.43 GHz
• GPU: 128-core Maxwell
• AI computing power: 473 GFLOPS (also stated as 472 GFLOP in the provided text)
• Low power operation: as little as 5 W (also shown as 5 W–10 W in provided comparison material)
• Video encode: 4K @ 30; 4x 1080p @ 30; 9x 720p @ 30 (H.264/H.265)
• Video decode: 4K @ 60; 2x 4K @ 30; 8x 1080p @ 30; 18x 720p @ 30 (H.264/H.265)
• Camera interface: MIPI CSI-2 DPHY channel *2
• Display: HDMI and DP
• Networking / expansion: Gigabit Ethernet; M.2 Key E; supports M.2 dual-band high-speed network card; supports USB high-speed network card
• USB: 4x USB 3.0; USB 2.0 Micro-B
• Other I/O listed: GPIO, I2C, I2S, SPI, UART
• Power inputs mentioned: micro USB, DC power, and PoE (as stated in the provided text)

Specifications

Storage Notes (Official Demo vs SUB Version)

• Provided comparison material lists two variants: Jetson Nano 4GB Developer Kit (SUB) and Jetson Nano 4GB Developer Kit (Official Demo).
• SUB storage (shown): 16GB eMMC. The material states the board can be started without external storage, and that 16GB eMMC meets regular development needs and is compatible with the official system image file.
• Official Demo storage (shown): microSD (Not include). The material states users need to purchase a TF card (microSD) and write the system image file to start the board.
• If storage capacity is insufficient for a project, the material states capacity can be expanded using a USB disk or TF card.

TF Card / System Image Note

• The provided material states: “The TF card included in the shipping list are all written with the system image.”
• The provided material also states: “All versions of the kit contain a 64GB TF card.”
• Another note states that installing the official system image + AI environment configuration may exceed 32GB, and it is recommended to use a U disk/TF card of 64GB or above.

Tutorial Videos

Course / Tutorial Content (as provided)

• Updated in June 2026: OpenClaw deployment and application tutorials (NEW). Two interaction methods are stated: WAP and voice modules.
• Advanced ROS Tutorials (HOT): ROS1 and ROS2 basics and related learning materials are shown.
• Advanced AI Vision Development Tutorials (HOT): includes items such as on-board camera tutorial, USB external camera test, Jetson-Inference environment construction, DeepStream environment construction, and more (as listed in the outline below).

Jetson Nano B01 Course Outline (extract)

• Get started quickly: 1. Learn the route; 2. Quick start tutorial
• Jetson Nano B01 Basic Tutorial: 1. Introduction to Jetson nano B01; 2. Flash the system image; 3. Flashed SD re-read disk; 4. Jetson Nano B01 starts; 5. Write the official image (SDK)
• Jetson Nano B01 SUB board basic tutorial: 1. Introduction to the Jetson Nano B01 SUB board; 2. Write EMMC system image
• TF start up: 1. Jetson Nano SUB TF card startup and scaling; 2. Write TF card system image; 3. Flashed SD re-read disk; 4. Write TF card boot
• U disk start up: 1. Write EMMC boot; 2. Write U disk system; 3. Flashed U disk re-read disk; 4. Jetson Nano SUB start up
• System basic setup tutorial: 1. Introduction to the Jetson Nano B01 system and desktop; 2. SD card expansion; 3. Network configuration; 4. SSH Telnet & File Transfer; 5. VNC remote login; 6. Jetson Nano B01 system backup; 7. Jetson Nano B01 swap space increased; 8. Installation and use of Jtop
• GPIO hardware control tutorial: 1. API usage of GPIO libraries; 2. Jetson Nano B01 hardware library configuration; 3. Pin reading function; 4. Pin level output control; 5. Control LED; 6. Jetson Nano B01 communicates with external device serial ports; 7. Jetson Nano B01 I2C communication
• AI advanced visual tutorial: 1. On-board camera tutorial; 2. USB external camera test; 3. Jupyter lab and Jetcham installation; 4. Install TensorFlow (optional); 5. Jetson-Inference environment construction (optional); 6. Hello AI World; 7. Image classification reasoning; 8. Object detection reasoning; 9. Semantic segmentation; 10. Pose estimation; 11. Action recognition; 12. Background removal; 13. Monocular depth estimation; 14. DeepStream environment construction (optional); 15. Automotive Inspection; 16. Introduction to yolo5; 17. YOLO5 environment construction (optional); 18. Real-time detection of yolo5; 19. yolo5 + tensorrt acceleration; 20. yolo5 + tensorrt acceleration + Deep Stream (open camera); 21. Mediapipe environment construction (optional); 22. Mediapipe development; 23. Read Me
• YOLOv11 / YOLO26 Advanced Usage (NEW): 00. Must read before running; 01. YOLOv11 environment construction; 02. CLI Usage; 03. Object Detection; 04. Instance Segmentation; 05. Pose Estimation; 06. Image Classification; 07. Oriented bounding box object detection; 08. Model conversion
• ROS1 basic course: 1. Introduction to ROS; 2. Project file structure; 3. Common commands and tools; 4. Publisher; 5. Subscribers; 6. Customize topic messages and use; 7. Client; 8. Server; 9. Customize service messages and usage; 10. TF release and monitoring
• ROS1 visual image processing course: 1. AR vision; 2. AR QR code; 3. ROS+OpenCV foundation; 4. ROS+OpenCV application; 5. MediaPipe development
• ROS2 basics course: 1. Introduction to ROS2; 2. ROS2 install Humble; 3. ROS2 development environment; 4. ROS2 workspace; 5. ROS2 function package; 6. ROS2 node; 7. ROS2 topic communication; 8. ROS2 service communication; 9. ROS2 action communication; 10. ROS2 custom interface message; 11. ROS2 parameter service case; 12. ROS2 meta-function package; 13. ROS2 distributed communication; 14. ROS2 DDS; 15. ROS2 time related API; 16. ROS2 common command tools; 17. ROS2 rviz2 use; 18. ROS2 rqt toolbox; 19. ROS2 Launch startup file configuration; 20. ROS2 recording and playback tool; 21. ROS2 URDF model; 22. ROS2 Gazebo simulation platform; 23. ROS2 TF2 coordinate transformation
• Docker Course: 1. Overview and installation; 2. Common commands; 3. Understand and publish images; 4. Hardware interaction data processing; 5. Enter docker container; 6. Update docker images
• OpenCV image processing course: 1. OpenCV Basic Course; 2. ROS+opencv application; 3. QR code recognition; 4. AR Vision; 5. Mediapipe
• Offline AI large model tutorials: 0. AI large model system image instructions; 1. AI large model environment deployment; 2. Install large model dialogue platform; 3. Meta AI Llama 3.2 model; 4. Alibaba Cloud Qwen2 model; 5. Alibaba Cloud Qwen3 model; 6. SUTD TinyLlama; 7. DeepSeek DeepSeek-R1 model; 8. Microsoft Phi-3; 9. Microsoft Orca Mini; 10. NVIDIA StarCoder2; 11. Google Gemma3 Visual Multimodal Large Model; 12. Offline Text to Speech (TTS); 13. Offline Speech to Text (ASR)
• Online large model tutorials: 1. OpenRouter large model API aggregation platform; 2. Multimodal visual understand application; 3. Multimodal visual localization application; 4. Multimodal table scanning application; 5. Multimodal autonomous proxy application
• Online large model (Voice interaction): 0. Voice interaction hardware connection (ReadMe); 1. Offline speech to text (ASR); 2. Offline text to speech (TTS); 3. AI large model voice interaction; 4. Multimodal visual understand speech interaction; 5. Multimodal visual position application; 6. Multimodal table scanning application; 7. Multimodal autonomous proxy application; 8. AI large model offline voice assistant
• OpenClaw deployment and basic usage: 1. OpenClaw Deployment; 2. OpenClaw WAP plugin application; 3. OpenClaw WebChat interaction; 4. OpenClaw TUI interaction; 5. OpenClaw tools introduction; 6. OpenClaw Gate gateway user manual; 7. OpenClaw features overview; 8. OpenClaw hub introduction (Skill installation); 9. OpenClaw application-file management; 10. OpenClaw application-camera; 12. OpenClaw application-script execution; 13. OpenClaw application-programming (Peripheral & GPIO Control); 14. OpenClaw application-dedicated AI Assistant
• OpenClaw preparation before use: 1. Peripheral hardware configuration; 2. OpenClaw API-KEY configuration; 3. OpenClaw switching model; 4. OpenClaw prompt words; 5. AI voice interaction configuration; 6. 3D scheme configuration tests
• OpenClaw Peripheral Act programming (Peripheral control): 1. Servo control; 2. RGB light strip; 3. OLED
• OpenClaw extension advanced development: 1. Temperature and humidity sensors; 2. Camera application; 1. Plant care butler; 2. AI-heat estimation; 3. AI-Guessing Palm Game; 4. AI Pet; 5. AI Meteorological Station; 6. Temperature sensitivity meter; 7. Scheduled tasks

Packing List (notes shown)

• Provided material states: separate board operation requires a power adapter and a 64G memory card.

Applications

• Edge AI prototyping: image classification, object detection, segmentation, speech processing
• ROS learning and robotics development (ROS system / ROS robot are shown as supported learning targets in the provided material)
• Computer vision and camera-based projects via MIPI CSI-2 (2 channels) or USB cameras (as referenced in the course outline)

For order confirmation (storage variant, included accessories) or integration questions (M.2 Key E WiFi cards, cameras, power), contact support@rcdrone.top or visit https://rcdrone.top/.

Details