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NVIDIA Jetson Nano Developer Kit / Module
The NVIDIA Jetson Nano Developer Kit is a compact, energy-efficient AI computing platform powered by a 128-core NVIDIA Maxwell GPU, enabling developers to run neural networks and AI applications on edge devices with minimal power consumption of just 5-10 watts. Machine learning engineers, roboticists, and IoT developers use this platform professionally to deploy computer vision, natural language processing, and autonomous systems on resource-constrained devices. It solves the critical problem of running sophisticated deep learning models locally on embedded systems without requiring cloud connectivity or expensive server infrastructure.
Product Overview
The NVIDIA Jetson Nano Developer Kit operates on the principle of edge AI computing, where neural network inference happens directly on the device rather than in the cloud. The kit features a quad-core ARM A57 CPU paired with a 128-core Maxwell GPU architecture, delivering 472 GFLOPS of GPU performance while consuming minimal power. The platform runs NVIDIA's JetPack SDK, which includes CUDA, cuDNN, TensorRT, and other AI frameworks optimized for the Jetson architecture. This combination allows developers to achieve real-time performance on tasks like object detection, image classification, and pose estimation while maintaining thermal efficiency through passive cooling designs.
What distinguishes the Jetson Nano is its exceptional price-to-performance ratio for AI development. Unlike traditional GPU computing platforms that require significant power infrastructure, the Nano operates on a single USB-C power connector, making it ideal for battery-powered robots, drones, and IoT gateways. The developer kit includes 4GB of LPDDR4 RAM, 16GB eMMC storage, and dual 100Mbps Gigabit Ethernet ports. Pre-trained models from NVIDIA's Model Zoo can be deployed immediately, while developers can fine-tune custom models using transfer learning techniques to achieve production-grade accuracy with minimal training data and computational resources.
Key Specifications
| Specification | Details |
| Product Type | Single Board Computer with Integrated GPU |
| Brand | NVIDIA |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| GPU | 128-core NVIDIA Maxwell Architecture, 472 GFLOPS |
| CPU | Quad-core ARM Cortex-A57 @ 1.43 GHz |
| RAM | 4GB LPDDR4 64-bit Memory |
| Storage | 16GB eMMC 5.1 Flash Storage |
| Power Consumption | 5-10W Typical Operation |
| Operating System | Linux (Ubuntu 18.04 LTS based) |
Key Features
- 128-Core Maxwell GPU delivering 472 GFLOPS for real-time AI inference on edge devices without cloud dependency
- Ultra-low power consumption of 5-10 watts enabling battery-powered robotics and IoT applications with extended runtime
- 4GB LPDDR4 RAM and 16GB eMMC storage providing sufficient capacity for multiple neural network models and edge AI workloads
- NVIDIA JetPack SDK pre-installed with CUDA, cuDNN, TensorRT, and OpenCV for accelerated AI development and deployment
- Dual Gigabit Ethernet ports supporting network connectivity for distributed edge computing and real-time data streaming
- 40-pin GPIO header enabling integration with sensors, actuators, and custom hardware for robotics and IoT projects
- USB 3.0 and USB 2.0 ports for high-speed peripheral connectivity including cameras, storage, and development tools
- CSI camera interface supporting multiple camera modules for computer vision applications at 30fps or higher
Applications and Use Cases
- Autonomous mobile robots and drones performing real-time object detection and navigation using TensorFlow or PyTorch models without cloud connectivity
- Smart video surveillance systems running person detection, activity recognition, and anomaly detection at the edge with sub-100ms latency
- Industrial IoT gateways processing sensor data streams and performing predictive maintenance using trained neural networks on manufacturing equipment
- Medical imaging applications including chest X-ray analysis, retinal scanning, and patient monitoring on portable edge devices in remote healthcare settings
- Retail analytics systems analyzing customer behavior, queue management, and inventory tracking through computer vision at point-of-sale locations
- Smart city infrastructure including traffic monitoring, parking management, and public safety applications with distributed edge processing
- Agricultural technology platforms monitoring crop health, pest detection, and irrigation optimization using multispectral imaging and deep learning
How to Use
Begin by unboxing your NVIDIA Jetson Nano Developer Kit and connecting the power supply via USB-C, along with an HDMI display, USB keyboard, and mouse. Insert a microSD card (64GB recommended) with JetPack OS pre-flashed using NVIDIA's official image on a host computer. Power on the device and complete the initial setup wizard to configure networking, user accounts, and system updates. The system will automatically download and install CUDA, cuDNN, and other AI libraries during first boot, which takes approximately 15-20 minutes depending on internet speed.
Once setup completes, access the Jetson Nano via SSH or direct desktop connection to begin developing AI applications. Start with NVIDIA's pre-trained models available in the Model Zoo, such as ResNet-50 for image classification or SSD-MobileNet for object detection. Use Python with TensorFlow, PyTorch, or ONNX Runtime to load and run inference on your models. For optimal performance, leverage TensorRT to quantize and optimize models, reducing inference latency by 2-3x compared to standard frameworks. Connect USB cameras or CSI camera modules for real-time computer vision applications, and utilize the GPIO pins for sensor integration and hardware control in robotics projects. Monitor system performance using jtop utility to track GPU utilization, temperature, and power consumption during development.
Frequently Asked Questions
What is the difference between NVIDIA Jetson Nano and Jetson Xavier NX?
The Jetson Nano features a 128-core Maxwell GPU with 472 GFLOPS, while the Xavier NX provides a 384-core Volta GPU with 1.4 TFLOPS, offering approximately 3x better performance. The Nano consumes 5-10 watts and costs significantly less, making it ideal for entry-level AI projects. The Xavier NX consumes 10-15 watts and suits applications requiring higher throughput, such as multi-model inference or 4K video processing. Choose Nano for educational projects, simple robotics, and cost-sensitive deployments; select Xavier NX for production systems requiring higher accuracy and real-time performance on complex models.
Can I run TensorFlow and PyTorch on Jetson Nano?
Yes, both frameworks are fully supported through NVIDIA's JetPack SDK. TensorFlow is pre-installed, while PyTorch requires a simple pip installation. However, ensure your models are optimized for the Nano's limited resources by using quantization, pruning, and knowledge distillation techniques. For best performance, convert models to TensorRT format, which provides 2-3x speedup through layer fusion and mixed-precision inference. Pre-trained models from TensorFlow Hub and PyTorch Model Zoo are directly compatible, though you may need to adjust batch sizes and input resolutions for real-time performance.
What camera options are compatible with Jetson Nano?
The Jetson Nano supports both USB cameras and CSI (Camera Serial Interface) camera modules. Popular CSI options include the Raspberry Pi Camera v2 and v3, IMX219 and IMX477 sensors, and NVIDIA's official Jetson Camera modules. USB cameras work out-of-the-box without additional configuration. For best performance in computer vision applications, CSI cameras are recommended as they provide lower latency and higher frame rates. Ensure your camera supports the resolution and frame rate required by your AI model, typically 640x480 at 30fps for real-time object detection applications.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy NVIDIA Jetson Nano Developer Kit / Module Online in India
Purchase the NVIDIA Jetson Nano Developer Kit / Module online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat. Get the best price on NVIDIA Jetson Nano Developer Kit / Module with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.
NVIDIA Jetson Nano Developer Kit / Module
The NVIDIA Jetson Nano Developer Kit is a compact, energy-efficient AI computing platform powered by a 128-core NVIDIA Maxwell GPU, enabling developers to run neural networks and AI applications on edge devices with minimal power consumption of just 5-10 watts. Machine learning engineers, roboticists, and IoT developers use this platform professionally to deploy computer vision, natural language processing, and autonomous systems on resource-constrained devices. It solves the critical problem of running sophisticated deep learning models locally on embedded systems without requiring cloud connectivity or expensive server infrastructure.
Product Overview
The NVIDIA Jetson Nano Developer Kit operates on the principle of edge AI computing, where neural network inference happens directly on the device rather than in the cloud. The kit features a quad-core ARM A57 CPU paired with a 128-core Maxwell GPU architecture, delivering 472 GFLOPS of GPU performance while consuming minimal power. The platform runs NVIDIA's JetPack SDK, which includes CUDA, cuDNN, TensorRT, and other AI frameworks optimized for the Jetson architecture. This combination allows developers to achieve real-time performance on tasks like object detection, image classification, and pose estimation while maintaining thermal efficiency through passive cooling designs.
What distinguishes the Jetson Nano is its exceptional price-to-performance ratio for AI development. Unlike traditional GPU computing platforms that require significant power infrastructure, the Nano operates on a single USB-C power connector, making it ideal for battery-powered robots, drones, and IoT gateways. The developer kit includes 4GB of LPDDR4 RAM, 16GB eMMC storage, and dual 100Mbps Gigabit Ethernet ports. Pre-trained models from NVIDIA's Model Zoo can be deployed immediately, while developers can fine-tune custom models using transfer learning techniques to achieve production-grade accuracy with minimal training data and computational resources.
Key Specifications
| Specification | Details |
| Product Type | Single Board Computer with Integrated GPU |
| Brand | NVIDIA |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| GPU | 128-core NVIDIA Maxwell Architecture, 472 GFLOPS |
| CPU | Quad-core ARM Cortex-A57 @ 1.43 GHz |
| RAM | 4GB LPDDR4 64-bit Memory |
| Storage | 16GB eMMC 5.1 Flash Storage |
| Power Consumption | 5-10W Typical Operation |
| Operating System | Linux (Ubuntu 18.04 LTS based) |
Key Features
- 128-Core Maxwell GPU delivering 472 GFLOPS for real-time AI inference on edge devices without cloud dependency
- Ultra-low power consumption of 5-10 watts enabling battery-powered robotics and IoT applications with extended runtime
- 4GB LPDDR4 RAM and 16GB eMMC storage providing sufficient capacity for multiple neural network models and edge AI workloads
- NVIDIA JetPack SDK pre-installed with CUDA, cuDNN, TensorRT, and OpenCV for accelerated AI development and deployment
- Dual Gigabit Ethernet ports supporting network connectivity for distributed edge computing and real-time data streaming
- 40-pin GPIO header enabling integration with sensors, actuators, and custom hardware for robotics and IoT projects
- USB 3.0 and USB 2.0 ports for high-speed peripheral connectivity including cameras, storage, and development tools
- CSI camera interface supporting multiple camera modules for computer vision applications at 30fps or higher
Applications and Use Cases
- Autonomous mobile robots and drones performing real-time object detection and navigation using TensorFlow or PyTorch models without cloud connectivity
- Smart video surveillance systems running person detection, activity recognition, and anomaly detection at the edge with sub-100ms latency
- Industrial IoT gateways processing sensor data streams and performing predictive maintenance using trained neural networks on manufacturing equipment
- Medical imaging applications including chest X-ray analysis, retinal scanning, and patient monitoring on portable edge devices in remote healthcare settings
- Retail analytics systems analyzing customer behavior, queue management, and inventory tracking through computer vision at point-of-sale locations
- Smart city infrastructure including traffic monitoring, parking management, and public safety applications with distributed edge processing
- Agricultural technology platforms monitoring crop health, pest detection, and irrigation optimization using multispectral imaging and deep learning
How to Use
Begin by unboxing your NVIDIA Jetson Nano Developer Kit and connecting the power supply via USB-C, along with an HDMI display, USB keyboard, and mouse. Insert a microSD card (64GB recommended) with JetPack OS pre-flashed using NVIDIA's official image on a host computer. Power on the device and complete the initial setup wizard to configure networking, user accounts, and system updates. The system will automatically download and install CUDA, cuDNN, and other AI libraries during first boot, which takes approximately 15-20 minutes depending on internet speed.
Once setup completes, access the Jetson Nano via SSH or direct desktop connection to begin developing AI applications. Start with NVIDIA's pre-trained models available in the Model Zoo, such as ResNet-50 for image classification or SSD-MobileNet for object detection. Use Python with TensorFlow, PyTorch, or ONNX Runtime to load and run inference on your models. For optimal performance, leverage TensorRT to quantize and optimize models, reducing inference latency by 2-3x compared to standard frameworks. Connect USB cameras or CSI camera modules for real-time computer vision applications, and utilize the GPIO pins for sensor integration and hardware control in robotics projects. Monitor system performance using jtop utility to track GPU utilization, temperature, and power consumption during development.
Frequently Asked Questions
What is the difference between NVIDIA Jetson Nano and Jetson Xavier NX?
The Jetson Nano features a 128-core Maxwell GPU with 472 GFLOPS, while the Xavier NX provides a 384-core Volta GPU with 1.4 TFLOPS, offering approximately 3x better performance. The Nano consumes 5-10 watts and costs significantly less, making it ideal for entry-level AI projects. The Xavier NX consumes 10-15 watts and suits applications requiring higher throughput, such as multi-model inference or 4K video processing. Choose Nano for educational projects, simple robotics, and cost-sensitive deployments; select Xavier NX for production systems requiring higher accuracy and real-time performance on complex models.
Can I run TensorFlow and PyTorch on Jetson Nano?
Yes, both frameworks are fully supported through NVIDIA's JetPack SDK. TensorFlow is pre-installed, while PyTorch requires a simple pip installation. However, ensure your models are optimized for the Nano's limited resources by using quantization, pruning, and knowledge distillation techniques. For best performance, convert models to TensorRT format, which provides 2-3x speedup through layer fusion and mixed-precision inference. Pre-trained models from TensorFlow Hub and PyTorch Model Zoo are directly compatible, though you may need to adjust batch sizes and input resolutions for real-time performance.
What camera options are compatible with Jetson Nano?
The Jetson Nano supports both USB cameras and CSI (Camera Serial Interface) camera modules. Popular CSI options include the Raspberry Pi Camera v2 and v3, IMX219 and IMX477 sensors, and NVIDIA's official Jetson Camera modules. USB cameras work out-of-the-box without additional configuration. For best performance in computer vision applications, CSI cameras are recommended as they provide lower latency and higher frame rates. Ensure your camera supports the resolution and frame rate required by your AI model, typically 640x480 at 30fps for real-time object detection applications.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy NVIDIA Jetson Nano Developer Kit / Module Online in India
Purchase the NVIDIA Jetson Nano Developer Kit / Module online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat. Get the best price on NVIDIA Jetson Nano Developer Kit / Module with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.