• NVIDIA Jetson Nano module with black gold circuitry and NVIDIA logo
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NVIDIA

NVIDIA 900-13448-0020-000 | Jetson Nano AI Module, Quad-Core A57, 128-Core GPU, 4GB

900-13448-0020-000

Stock Status: Enquire

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Description

The NVIDIA Jetson Nano Module is a compact system-on-module designed for edge AI and embedded computing applications. Powered by a quad-core ARM Cortex-A57 CPU and 128-core Maxwell GPU with 4GB LPDDR4 memory, it delivers up to 472 GFLOPS of AI computing performance. With 16GB onboard eMMC storage and support for 5W/10W power modes, it is ideal for robotics, industrial automation, smart cameras, and IoT gateway deployments requiring real-time neural network inference.

Features

NVIDIA Maxwell GPU with 128 CUDA cores for parallel AI workloads
- Quad-core ARM Cortex-A57 CPU complex with 64-bit architecture
- 4GB LPDDR4 memory with 25.6 GB/s bandwidth for high-throughput data processing
- Hardware-accelerated video encoding and decoding up to 4K resolution
- Multiple high-speed interfaces: PCIe Gen2, USB 3.0, Gigabit Ethernet
- Rich I/O support: UART, SPI, I2C, I2S, and configurable GPIO
- 12-lane MIPI CSI-2 camera interface for multi-sensor vision systems
- Dual display outputs: HDMI 2.0 and DisplayPort 1.2
- Software-configurable 5W and 10W power modes for efficiency optimization
- 260-pin SO-DIMM form factor for integration into custom carrier boards
- Support for TensorRT, CUDA, cuDNN for accelerated deep learning
- JetPack SDK with comprehensive libraries for AI, computer vision, and multimedia
- Linux for Tegra (L4T) operating system based on Ubuntu

Warranty

All products sold by XS Network Tech include a 12-month warranty on both new and used items. Our in-house technical team thoroughly tests used hardware prior to sale to ensure enterprise-grade reliability.

All technical data should be verified on the manufacturer data sheets.

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Technical Specifications

FAQs

Technical Specifications

CPU: Quad-core ARM Cortex-A57 @ 1.43 GHz
- GPU: 128-core NVIDIA Maxwell architecture
- Memory: 4GB LPDDR4 (25.6 GB/s)
- Storage: 16GB eMMC 5.1
- AI Performance: 472 GFLOPS (FP16)
- Power Modes: 5W and 10W software-defined modes
- Video Encode: 4K @ 30 fps (H.264/H.265)
- Video Decode: 4K @ 60 fps (H.264/H.265)
- Camera: 12 lanes MIPI CSI-2 D-PHY 1.1
- Connectivity Interfaces: PCIe Gen2 x4, USB 3.0, Gigabit Ethernet
- I/O: 3x UART, 2x SPI, 4x I2C, 2x I2S, GPIO
- Display: HDMI 2.0 and DisplayPort 1.2
- Operating Temperature: 0°C to 50°C
- Module Form Factor: 260-pin SO-DIMM
- Dimensions: 69.6mm x 45mm

FAQs

Q: What AI frameworks are supported on the Jetson Nano Module?
A: The module is production-ready and supports all popular AI frameworks including TensorFlow, PyTorch, Caffe, MXNet, and others through the NVIDIA JetPack SDK. It includes accelerated libraries for deep learning inference via TensorRT, computer vision with OpenCV, and GPU computing through CUDA.

Q: What are the two power modes and how do they work?
A: The Jetson Nano Module features two software-defined power modes: 5W mode and 10W mode. These modes dynamically adjust CPU and GPU frequencies and the number of active CPU cores to optimize performance within the selected power budget, allowing flexibility for different deployment scenarios.

Q: What interfaces are available for camera connectivity?
A: The module provides 12 lanes of MIPI CSI-2 D-PHY 1.1 interface, enabling connection of multiple high-resolution cameras simultaneously. This is essential for applications requiring multi-sensor AI processing such as autonomous systems, optical inspection, and video analytics.

Q: Can the Jetson Nano Module run multiple neural networks simultaneously?
A: Yes, the module can run multiple modern neural networks in parallel, making it suitable for complex AI applications that require simultaneous processing tasks such as object detection, classification, and segmentation in real-time edge computing scenarios.

Q: What are typical use cases for this module?
A: The Jetson Nano Module is designed for edge AI applications including smart cameras, network video recorders, industrial optical inspection systems, autonomous mobile robots, AIoT gateways, and embedded vision systems requiring real-time neural network inference at the edge.

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