44 % छूट
NVIDIA Jetson Nano Developer Kit - B01
The NVIDIA Jetson Nano Developer Kit B01 is a compact, energy-efficient AI computing platform powered by NVIDIA's 128-core Maxwell GPU, designed to enable developers to build intelligent edge applications with real-time inference capabilities. Professional machine learning engineers, roboticists, and IoT developers use this platform to prototype and deploy computer vision, natural language processing, and autonomous systems on resource-constrained edge devices. This kit solves the critical problem of running sophisticated deep learning models locally on embedded systems without relying on cloud connectivity, reducing latency and enabling privacy-preserving AI applications in production environments.
Product Overview
The Jetson Nano B01 Developer Kit delivers professional-grade AI acceleration in a credit-card-sized form factor, consuming just 5-10 watts of power while providing up to 472 GFLOPS of peak performance for FP32 operations and 1.5 TFLOPS for INT8 operations. The platform features 4GB of LPDDR4 RAM, 16GB of eMMC storage, and a quad-core ARM Cortex-A57 processor running at 1.43 GHz, making it ideal for deploying pre-trained neural networks and custom machine learning models at the edge. The architecture leverages NVIDIA's CUDA ecosystem and cuDNN libraries, enabling developers to leverage the same deep learning frameworks (TensorFlow, PyTorch, Keras) used in high-performance computing environments, but optimized for embedded deployment with significantly reduced memory footprint and power consumption.
What distinguishes the B01 revision is its improved thermal design with a larger heatspreader and refined power delivery system, providing superior stability during sustained inference workloads compared to the original A02 variant. The kit includes a comprehensive software stack with JetPack SDK, pre-installed CUDA 10.2 toolkit, cuDNN 7.6, and TensorRT for optimized inference, eliminating extensive configuration overhead. With dual USB 3.0 ports, Gigabit Ethernet, 40-pin GPIO header, and CSI camera connector supporting up to 2 cameras, developers have extensive connectivity options for integrating sensors, actuators, and peripherals in real-world applications ranging from autonomous drones to industrial vision systems.
Key Specifications
| Specification | Details |
| Product Type | AI Edge Computing Developer Kit |
| 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 | NVIDIA Maxwell Architecture, 128 CUDA Cores |
| CPU | Quad-core ARM Cortex-A57, 1.43 GHz |
| RAM | 4GB LPDDR4, 25.6 GB/s bandwidth |
| Storage | 16GB eMMC 5.1 Flash Memory |
| Peak Performance | 472 GFLOPS (FP32), 1.5 TFLOPS (INT8) |
| Power Consumption | 5-10 watts typical operation |
Key Features
- Maxwell GPU Architecture with 128 CUDA cores enabling parallel processing of deep learning inference tasks with 10x better energy efficiency compared to CPU-only solutions
- NVIDIA JetPack SDK pre-installed with CUDA 10.2, cuDNN 7.6, and TensorRT optimization engine for deploying production-ready AI models with minimal latency
- Dual USB 3.0 ports, Gigabit Ethernet, and 40-pin GPIO header providing extensive connectivity for integrating cameras, sensors, and IoT devices in edge computing applications
- Compact form factor measuring 100x80mm with passive cooling design, operating reliably in industrial environments without active fans or complex thermal management
- Support for multiple deep learning frameworks including TensorFlow, PyTorch, Keras, and Caffe, allowing seamless model porting from development to edge deployment
- Improved B01 thermal design with enhanced heatspreader providing superior sustained performance during continuous inference workloads
Applications and Use Cases
- Autonomous robotics and drone applications where real-time object detection and path planning require local GPU acceleration without cloud dependency or network latency constraints
- Industrial computer vision systems for quality inspection, defect detection, and visual guidance in manufacturing environments requiring sub-100ms inference latency
- Smart surveillance and edge analytics platforms processing video streams locally for person detection, activity recognition, and anomaly detection with privacy preservation
- Healthcare IoT devices including portable ultrasound analysis, medical image classification, and patient monitoring systems requiring embedded AI inference with minimal power consumption
- Intelligent agriculture solutions for crop disease detection, pest identification, and yield prediction using onboard image processing without cloud connectivity
- Natural language processing applications including voice assistants, sentiment analysis, and real-time translation on edge devices
How to Use
Begin by flashing the JetPack image to a microSD card (minimum 32GB recommended) using the provided Jetson Nano flashing utility on a Linux host computer. Insert the microSD card into the Jetson Nano, connect a USB power supply (5V/4A minimum), HDMI display, USB keyboard and mouse, and boot the system. The pre-installed Ubuntu 18.04 LTS environment with CUDA 10.2 and cuDNN libraries will be immediately available, allowing you to verify GPU functionality using nvidia-smi command and run sample CUDA programs from the /usr/local/cuda/samples directory.
For deploying your own deep learning models, export trained networks from TensorFlow or PyTorch to ONNX format, then use NVIDIA's TensorRT optimization tool to convert models for inference on the Jetson Nano's Maxwell GPU. This optimization process typically reduces model size by 50-80% and inference latency by 3-5x compared to running on CPU alone. Connect USB cameras or CSI ribbon cameras to the dedicated camera connector, install OpenCV with CUDA support, and leverage the provided Python APIs and C++ libraries to build real-time vision applications. The Jetson Nano Developer Kit includes comprehensive documentation, sample projects, and an active community forum for troubleshooting integration challenges.
Frequently Asked Questions
What is the difference between Jetson Nano A02 and B01 revisions?
The B01 revision features an improved thermal design with a larger heatspreader and refined power delivery circuitry, providing superior sustained performance during continuous inference workloads compared to the original A02 variant. The B01 offers better stability when running demanding deep learning models for extended periods without thermal throttling. Both variants share identical GPU and CPU specifications, but B01 is recommended for production deployments requiring consistent performance.
Can I run multiple neural networks simultaneously on the Jetson Nano?
Yes, the Jetson Nano's 128 CUDA cores can execute multiple inference tasks concurrently through NVIDIA's CUDA streaming and multi-threading capabilities. However, performance will degrade proportionally with the number of parallel models. For optimal results, profile your workload using nvidia-smi to monitor GPU utilization and memory bandwidth. Most developers achieve best results running 1-2 optimized TensorRT models simultaneously while maintaining real-time performance requirements.
What power supply specifications are required for the Jetson Nano B01?
The Jetson Nano B01 requires a 5V DC power supply with minimum 4A current capacity for stable operation under full GPU load. We recommend using official NVIDIA power adapters or certified third-party supplies meeting USB Power Delivery specifications. Inadequate power supplies may cause system crashes, corrupted storage, or hardware damage. The kit includes a barrel connector for direct power input, bypassing the USB port for more reliable power delivery in production environments.
Is the Jetson Nano suitable for real-time video processing at 30 FPS?
Yes, the Jetson Nano can process video streams at 30 FPS for many computer vision tasks including object detection, semantic segmentation, and pose estimation using optimized TensorRT models. Performance depends on model complexity and input resolution. For example, a quantized MobileNet-based detector achieves 30+ FPS at 640x480 resolution, while larger ResNet models may require lower frame rates or resolution reduction. Always profile your specific model using NVIDIA's profiling tools before deployment.
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 - B01 Online in India
Purchase the NVIDIA Jetson Nano Developer Kit - B01 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 - B01 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 - B01
The NVIDIA Jetson Nano Developer Kit B01 is a compact, energy-efficient AI computing platform powered by NVIDIA's 128-core Maxwell GPU, designed to enable developers to build intelligent edge applications with real-time inference capabilities. Professional machine learning engineers, roboticists, and IoT developers use this platform to prototype and deploy computer vision, natural language processing, and autonomous systems on resource-constrained edge devices. This kit solves the critical problem of running sophisticated deep learning models locally on embedded systems without relying on cloud connectivity, reducing latency and enabling privacy-preserving AI applications in production environments.
Product Overview
The Jetson Nano B01 Developer Kit delivers professional-grade AI acceleration in a credit-card-sized form factor, consuming just 5-10 watts of power while providing up to 472 GFLOPS of peak performance for FP32 operations and 1.5 TFLOPS for INT8 operations. The platform features 4GB of LPDDR4 RAM, 16GB of eMMC storage, and a quad-core ARM Cortex-A57 processor running at 1.43 GHz, making it ideal for deploying pre-trained neural networks and custom machine learning models at the edge. The architecture leverages NVIDIA's CUDA ecosystem and cuDNN libraries, enabling developers to leverage the same deep learning frameworks (TensorFlow, PyTorch, Keras) used in high-performance computing environments, but optimized for embedded deployment with significantly reduced memory footprint and power consumption.
What distinguishes the B01 revision is its improved thermal design with a larger heatspreader and refined power delivery system, providing superior stability during sustained inference workloads compared to the original A02 variant. The kit includes a comprehensive software stack with JetPack SDK, pre-installed CUDA 10.2 toolkit, cuDNN 7.6, and TensorRT for optimized inference, eliminating extensive configuration overhead. With dual USB 3.0 ports, Gigabit Ethernet, 40-pin GPIO header, and CSI camera connector supporting up to 2 cameras, developers have extensive connectivity options for integrating sensors, actuators, and peripherals in real-world applications ranging from autonomous drones to industrial vision systems.
Key Specifications
| Specification | Details |
| Product Type | AI Edge Computing Developer Kit |
| 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 | NVIDIA Maxwell Architecture, 128 CUDA Cores |
| CPU | Quad-core ARM Cortex-A57, 1.43 GHz |
| RAM | 4GB LPDDR4, 25.6 GB/s bandwidth |
| Storage | 16GB eMMC 5.1 Flash Memory |
| Peak Performance | 472 GFLOPS (FP32), 1.5 TFLOPS (INT8) |
| Power Consumption | 5-10 watts typical operation |
Key Features
- Maxwell GPU Architecture with 128 CUDA cores enabling parallel processing of deep learning inference tasks with 10x better energy efficiency compared to CPU-only solutions
- NVIDIA JetPack SDK pre-installed with CUDA 10.2, cuDNN 7.6, and TensorRT optimization engine for deploying production-ready AI models with minimal latency
- Dual USB 3.0 ports, Gigabit Ethernet, and 40-pin GPIO header providing extensive connectivity for integrating cameras, sensors, and IoT devices in edge computing applications
- Compact form factor measuring 100x80mm with passive cooling design, operating reliably in industrial environments without active fans or complex thermal management
- Support for multiple deep learning frameworks including TensorFlow, PyTorch, Keras, and Caffe, allowing seamless model porting from development to edge deployment
- Improved B01 thermal design with enhanced heatspreader providing superior sustained performance during continuous inference workloads
Applications and Use Cases
- Autonomous robotics and drone applications where real-time object detection and path planning require local GPU acceleration without cloud dependency or network latency constraints
- Industrial computer vision systems for quality inspection, defect detection, and visual guidance in manufacturing environments requiring sub-100ms inference latency
- Smart surveillance and edge analytics platforms processing video streams locally for person detection, activity recognition, and anomaly detection with privacy preservation
- Healthcare IoT devices including portable ultrasound analysis, medical image classification, and patient monitoring systems requiring embedded AI inference with minimal power consumption
- Intelligent agriculture solutions for crop disease detection, pest identification, and yield prediction using onboard image processing without cloud connectivity
- Natural language processing applications including voice assistants, sentiment analysis, and real-time translation on edge devices
How to Use
Begin by flashing the JetPack image to a microSD card (minimum 32GB recommended) using the provided Jetson Nano flashing utility on a Linux host computer. Insert the microSD card into the Jetson Nano, connect a USB power supply (5V/4A minimum), HDMI display, USB keyboard and mouse, and boot the system. The pre-installed Ubuntu 18.04 LTS environment with CUDA 10.2 and cuDNN libraries will be immediately available, allowing you to verify GPU functionality using nvidia-smi command and run sample CUDA programs from the /usr/local/cuda/samples directory.
For deploying your own deep learning models, export trained networks from TensorFlow or PyTorch to ONNX format, then use NVIDIA's TensorRT optimization tool to convert models for inference on the Jetson Nano's Maxwell GPU. This optimization process typically reduces model size by 50-80% and inference latency by 3-5x compared to running on CPU alone. Connect USB cameras or CSI ribbon cameras to the dedicated camera connector, install OpenCV with CUDA support, and leverage the provided Python APIs and C++ libraries to build real-time vision applications. The Jetson Nano Developer Kit includes comprehensive documentation, sample projects, and an active community forum for troubleshooting integration challenges.
Frequently Asked Questions
What is the difference between Jetson Nano A02 and B01 revisions?
The B01 revision features an improved thermal design with a larger heatspreader and refined power delivery circuitry, providing superior sustained performance during continuous inference workloads compared to the original A02 variant. The B01 offers better stability when running demanding deep learning models for extended periods without thermal throttling. Both variants share identical GPU and CPU specifications, but B01 is recommended for production deployments requiring consistent performance.
Can I run multiple neural networks simultaneously on the Jetson Nano?
Yes, the Jetson Nano's 128 CUDA cores can execute multiple inference tasks concurrently through NVIDIA's CUDA streaming and multi-threading capabilities. However, performance will degrade proportionally with the number of parallel models. For optimal results, profile your workload using nvidia-smi to monitor GPU utilization and memory bandwidth. Most developers achieve best results running 1-2 optimized TensorRT models simultaneously while maintaining real-time performance requirements.
What power supply specifications are required for the Jetson Nano B01?
The Jetson Nano B01 requires a 5V DC power supply with minimum 4A current capacity for stable operation under full GPU load. We recommend using official NVIDIA power adapters or certified third-party supplies meeting USB Power Delivery specifications. Inadequate power supplies may cause system crashes, corrupted storage, or hardware damage. The kit includes a barrel connector for direct power input, bypassing the USB port for more reliable power delivery in production environments.
Is the Jetson Nano suitable for real-time video processing at 30 FPS?
Yes, the Jetson Nano can process video streams at 30 FPS for many computer vision tasks including object detection, semantic segmentation, and pose estimation using optimized TensorRT models. Performance depends on model complexity and input resolution. For example, a quantized MobileNet-based detector achieves 30+ FPS at 640x480 resolution, while larger ResNet models may require lower frame rates or resolution reduction. Always profile your specific model using NVIDIA's profiling tools before deployment.
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 - B01 Online in India
Purchase the NVIDIA Jetson Nano Developer Kit - B01 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 - B01 with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.