--- product_id: 725449388 title: "Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version)" brand: "waypondev" price: "KD 16385.56" currency: KWD in_stock: true reviews_count: 4 url: https://www.desertcart.com.kw/products/725449388-jetson-nano-4gb-ram-16g-emmc-onboard-for-ai-robotics store_origin: KW region: Kuwait --- # 5W ultra-low power consumption 16GB onboard eMMC storage 128-core NVIDIA Maxwell GPU Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) **Brand:** waypondev **Price:** KD 16385.56 **Availability:** βœ… In Stock ## Summary > πŸ€– Power your AI dreams with Jetson Nano β€” small board, giant leaps! ## Quick Answers - **What is this?** Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) by waypondev - **How much does it cost?** KD 16385.56 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.com.kw](https://www.desertcart.com.kw/products/725449388-jetson-nano-4gb-ram-16g-emmc-onboard-for-ai-robotics) ## Best For - waypondev enthusiasts ## Why This Product - Trusted waypondev brand quality - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Key Features - β€’ **Energy-Smart Design:** Maximize performance while consuming just 5 wattsβ€”perfect for sustainable innovation. - β€’ **Versatile Connectivity:** Expand your AI ecosystem with 5 USB 3.0 ports, Gigabit Ethernet, and multiple sensor interfaces. - β€’ **Pro-Grade Developer Kit:** Includes cooling fan, reference carrier board, and 5V 4A power supply for smooth, reliable builds. - β€’ **Plug & Play AI Powerhouse:** Launch AI projects instantly with built-in 16GB eMMCβ€”no TF card needed. - β€’ **Effortless Multi-Framework Support:** Seamlessly run TensorFlow, PyTorch, and more with JetPack compatibility. ## Overview The WayPonDEV Jetson Nano 4GB SUB Kit is a compact AI developer board featuring a 128-core NVIDIA Maxwell GPU, quad-core ARM A57 CPU, and 16GB onboard eMMC storage for instant startup without a TF card. It supports multiple AI frameworks via JetPack SDK, offers ultra-low 5W power consumption, and includes versatile connectivity options like USB 3.0, Gigabit Ethernet, and camera interfaces. Designed for professionals and makers, it comes with a cooling fan, reference carrier board, and 5V 4A power supply to accelerate AI and robotics development. ## Description -Overview- The Jetson Nano 4GB Developer Kit (Official Demo) and the Jetson Nano 4GB Developer Kit (SUB) have the same computing performance, onboard resources, sizes, and interface layout. The only difference is that the SUB version comes with 16GEMMC storage which can be launched easily and quickly without a TF card. The Jetson Nano SUB Kit Nano allows the development of millions of new small, low-power AI systems. Open a new world of embedded IoT applications, including entry-level network video recorders (NVR), home robot and intelligent gateway with full analysis. -Specifications- GPU: 128 Core NVIDIA Maxwell CPU: Quad-core ARM A57 @ 1.43GHz Memory: 4GB 64-bit LPDDR4 25.6GB/s Storage: 16GB eMMC Video Encode: 4Kp @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265) Video Decode: 4Kp @ 60 | 2x 4Kp @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265) Camera: 2x MIPI CSI-2 DPHY Lane Connectivity: Gigabit Ethernet, M.2 key E Display: HDMI 2.0, eDP 1.4 USB: 4x USB 3.0, USB 2.0 Micro-B Other: GPIO, I2C, I2S, SPI, UART Power options: >Micro-USB 5V 2A, DC power adapter 5V 4A. Dimensions: 100mm x 80mm x 29mm -Package List: 1 x Jetson Nano Module (SUB Kit Nano ) with Cooling Fan and Reference Carrier Board 1 x 5V 4A Power Supply -Tech-Team- monica#smartfire.cn(#----->@) Review: Tons of problems. A giant waste of time! Icould not find a REAL B01 from Nvidia, so I bought this one, a clone. It's a mistake! I am not a novice and can find my way around, but this product is totally hopeless! It only can use Ubuntu 18.04! I started out with Ubuntu 22.04, then 20.04, but had to go back all the way to 18.04 (had to buy 2 500GB SSD to do this! ) Then there is not enough storage on the eMMc to do anything! Can't get it to expand to the 512GB SD card I use! During all this struggle, the eMMc got corrupted and I spent tons of time trying to get inside the eMMc to debug/flash it! No luck! The Nvidia SDK cannot see the Nano board! Don't even try what I did. The vendor has zero support. All wiki/on line suggestions are worthless. I was lucky I could return it just before running out of time. Review: Bought a Chinese knock off first and had problems. Must have some understanding of how to update to JetPack 6 to make this operate. So buy the NVIDA as there is good online documentation. No support given from the Chinese company. ## Features - [Note] The Jetson Nano 4GB SUB Kit Nano is a consistent developer kit based on the official Jetson nano 4GB core module. The only difference between the official version is that the WayPonDEV Jetson Nano 4GB SUB Kit comes with 16G-eMMC memory so you can start your board without a TF card. It also has TF card slot, you can use the USB 3.0 U disk to expand your memory - [Multiple AI Framework] WayPonDev Jetson Nano SUB Kit Nano provides compute performance for running the latest AI workloads in unprecedented size, power, and cost. Developers, learners, and manufacturers are now able to perform AI framework and models for applications such as image classification, object detection, segmentation, and voice processing - [Low Consumption] This developer kit is powered by micro USB and comes with a wide range of I/Os from GPIO to CSI. This allows developers to easily connect a variety of new sensor sets to achieve a variety of AI applications. Extremely energy-efficient and consumes only 5 watts - [JetPack Compatible] The Jetson Nano Sub Kit Nano is also supported by JetPack. Includes board support package (BSP), Linux OS, N-VI-DI-A CUDA, cuDNN, and TensorRT software library for deep learning, computer vision, GPU computing, multimedia processing and more. You can also use software to use the SD card image that is easy to flash Easy to get started - [Easy-to-use N-V-I-DIA Official AI Software] The same JetPack SDK is used throughout the Jetson product family, and is fully compatible with the world's leading AI platform for training and deployment of AI software. This proven software stack reduces developer complexity and overall effort - [IN THE BOX] 1 x Jetson Nano Module with heatsink, 1 x Reference Carrier Board (SUB Kit Nano ), 1 x 5V-4A Power Supply If you have any questions, please click "WayPonDEV" to ask us at wpd#youyeetoo.com (#------>@) ## Technical Specifications | Specification | Value | |---------------|-------| | Total Usb Ports | 5 | | Compatible Devices | PC | | Wireless Comm Standard | 802.11b | | Batteries Included | No | | Batteries Required | No | | Brand | WayPonDEV | | Manufacturer | WayPonDEV | | Item Model Number | Jetson Nano 4GB Developer Kit | | Included Components | 1 x Jetson Nano Module (SUB Kit Nano ) with Cooling Fan and Reference Carrier Board and 1 x 5V 4A Power Supply | | Operating System | Linux | | Connectivity Technology | Ethernet | | Product Dimensions | 10.01 x 8 x 2.9 cm; 472 g | ## Product Details - **Brand:** WayPonDEV - **Model Name:** Jetson Nano 4GB SUB Developer Kit (Heat Sink Version) - **Ram Memory Installed Size:** 4 GB - **Memory Storage Capacity:** 16 GB - **CPU Speed:** 1.43 GHz ## Images ![Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) - Image 1](https://m.media-amazon.com/images/I/71Aav0GZEBL.jpg) ![Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) - Image 2](https://m.media-amazon.com/images/I/71QiN3saeFL.jpg) ![Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) - Image 3](https://m.media-amazon.com/images/I/71WA4FU+MyL.jpg) ![Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) - Image 4](https://m.media-amazon.com/images/I/81lw5bK5h4L.jpg) ![Jetson Nano 4GB RAM 16G eMMC Onboard for AI Robotics Machine Learning (Heatsink Version) - Image 5](https://m.media-amazon.com/images/I/71E1TMuuQPL.jpg) ## Customer Reviews ### ⭐ Review *by L***I on April 25, 2025* Tons of problems. A giant waste of time! Icould not find a REAL B01 from Nvidia, so I bought this one, a clone. It's a mistake! I am not a novice and can find my way around, but this product is totally hopeless! It only can use Ubuntu 18.04! I started out with Ubuntu 22.04, then 20.04, but had to go back all the way to 18.04 (had to buy 2 500GB SSD to do this! ) Then there is not enough storage on the eMMc to do anything! Can't get it to expand to the 512GB SD card I use! During all this struggle, the eMMc got corrupted and I spent tons of time trying to get inside the eMMc to debug/flash it! No luck! The Nvidia SDK cannot see the Nano board! Don't even try what I did. The vendor has zero support. All wiki/on line suggestions are worthless. I was lucky I could return it just before running out of time. ### ⭐⭐⭐⭐⭐ Review *by K***R on September 16, 2024* Bought a Chinese knock off first and had problems. Must have some understanding of how to update to JetPack 6 to make this operate. So buy the NVIDA as there is good online documentation. No support given from the Chinese company. ### ⭐⭐⭐ Review *by T***Z on January 27, 2025* It does have a problem when updating and also have the problem with booting from the SD card. It doesn't have any clear instruction or manual inside. I first update the system and install some packages, but then it has problem after rebooting. I'm afraid the onboard eMMC storage/filesystem is corrupted. The suggested solution I googled says the eMMC should be reflashed. But the situation is I don't have Linux host available except for vm-linux. It doesn't look working. Now the booting gets stuck at somewhere around "Bridge firewalling registered". I tried to jump in bootloader stage to change the env variable, but it doesn't provide such the capability. The experience is so bad considering it's of Nvidia product. My initial feeling is Nvidia or this vendor doesn't consider the eMMC storage running-out situation and might go through some update during or after rebooting, then it causes the problem and cannot go back to previous good base point. Ted ## Frequently Bought Together - Jetson Nano 4GB RAM 16G eMMC onboard for AI Robotics Machine Learning (Heat Sink Version) - Wireless-AC8265 Dual Mode AC8265 Wireless NIC Module for Jetson Nano Developer Kit M.2 NGFF Support 2.4GHz / 5GHz 300Mbps / 867Mbps Dual Band WiFi and Bluetooth 4.2 - Yahboom Jetson Case for Jetson Nano Orin Nano Orin NX Super Heat Metal Mini Protect Case with Cooling Fan Antenna RGB Light(OLED Needs Program Drive) --- ## Why Shop on Desertcart? - πŸ›’ **Trusted by 1.3+ Million Shoppers** β€” Serving international shoppers since 2016 - 🌍 **Shop Globally** β€” Access 737+ million products across 21 categories - πŸ’° **No Hidden Fees** β€” All customs, duties, and taxes included in the price - πŸ”„ **15-Day Free Returns** β€” Hassle-free returns (30 days for PRO members) - πŸ”’ **Secure Payments** β€” Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** β€” Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.com.kw/products/725449388-jetson-nano-4gb-ram-16g-emmc-onboard-for-ai-robotics](https://www.desertcart.com.kw/products/725449388-jetson-nano-4gb-ram-16g-emmc-onboard-for-ai-robotics) --- *Product available on Desertcart Kuwait* *Store origin: KW* *Last updated: 2026-05-21*