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- Jetson Nano Developer Kit Nano B01 with 16G-eMMC Based on Official N-VI-Dia Jetson Nano 4GB Core Module
Jetson Nano Developer Kit Nano B01 with 16G-eMMC Based on Official N-VI-Dia Jetson Nano 4GB Core Module
IDR 9191027
Price Details
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The Jetson Nano 4GB SUB Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost.
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What Stands Out
Rincian Produk
- Yahboom Jetson Nano 4GB SUB Developer Kit is a consistent alternative to the official Jetson Nano 4GB core module
- The SUB motherboard comes with 16G-eMMC memory, eliminating the need for a separate TF card
- The developer kit delivers high compute performance for running AI workloads like image classification, object detection, and speech processing
- Powered by micro-USB, it features extensive I/Os from GPIO to CSI for connecting new sensors and enabling various AI applications
- Supported by JetPack, including Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries for deep learning and computer vision
- Please note that the SUB does not have an SD card slot, and memory expansion is possible only through a U disk
| Item Weight | 3 lbs (1.36 kg) |
Who Should Buy?
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AI Enthusiasts
Perfect for hobbyists eager to explore AI projects and machine learning applications on an affordable, compact platform.
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Developers
Ideal for software developers looking to prototype and test AI algorithms and applications using NVIDIA's powerful architecture.
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Educational Use
Great for universities and educational institutions implementing hands-on learning for students in robotics and AI technologies.
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Gaming Users
Not suitable for high-performance gaming needs as it is designed primarily for AI and embedded applications.
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Basic Computing
Inadequate for general computing tasks like web browsing or standard office work, designed specifically for specialized applications.
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Casual Users
Not ideal for casual users looking for plug-and-play devices; requires programming knowledge to utilize effectively.
DESKRIPSI PRODUK
Jetson Nano Developer Kit Nano B01 with 16G-eMMC Based on Official N-VI-Dia Jetson Nano 4GB Core Module
Tanya & Jawab Pelanggan
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pertanyaan:
What is the difference between Yahboom Jetson Nano and official Jetson Nano?
menjawab: Yahboom Jetson Nano 4GB SUB is a highly consistent developer kit based on official Jetson nano 4GB core module, the only difference is that Yahboom Jetson Nano 4GB SUB motherboard comes with 16G-eMMC memory and does not have an SD card slot. -
pertanyaan:
What are the AI applications it can perform?
menjawab: It can run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing. -
pertanyaan:
What is the power consumption of the developer kit?
menjawab: It is incredibly power-efficient, consuming as little as 5 watts and can be powered by micro-USB.
Yahboom Single-Board Computers & Accessories Editorial Review
Customer Reviews & Ratings
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IDR 9191027
Pesan sekarang dan dapatkan kira-kira pada Kamis, Juli 23
This item is not restrict in my country.(Please click on above link if this item is not restrict in your country, So our team will review and allow.)
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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Fitur & Manfaat
- Yahboom Jetson Nano Developer Kit Nano B01 is a perfect alternative to official Jetson nano 4GB core module
- It includes 16G-eMMC memory and can expand the USB3.0 U disk
- It can run AI frameworks and models for image classification, object detection, segmentation, and speech processing
- It is power-efficient, consuming as little as 5 watts and can be powered by micro-USB
- The kit comes with extensive I/Os, ranging from GPIO to CSI
- Jetson Nano is supported by JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries.
