• This is the module version of Jetson Nano — not the developer-kit with carrier board, but the bare System-on-Module (SoM) identified by PN 900-13448-0020-000.
• Intended for integration into custom carrier boards or embedded devices (cameras, AI-oT devices, robots, etc.), rather than as a ready-to-use evaluation board.
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Key Specifications
• GPU: 128-core NVIDIA Maxwell GPU
• CPU: Quad-core ARM Cortex-A57 MPCore processor
• Memory: 4 GB 64-bit LPDDR4 — 25.6 GB/s bandwidth.
• Storage (on module): 16 GB eMMC 5.1 flash.
• Networking: 10/100/1000 BASE-T Ethernet (Gigabit) MAC built-in; carrier board or external PHY required to expose.
• Video / Multimedia: Supports hardware video encode & decode (H.264/H.265), suitable for video-AI / streaming / vision tasks.
• Interfaces on module: 260-pin edge connector (SODIMM-style), supporting a rich set of I/O when mounted on a carrier board — PCIe, MIPI-CSI, USB, GPIO, I²C/SPI, and more.
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What It’s Good For / Use-Cases
Because of its modest but capable hardware, the Jetson Nano module is a good fit for:
• Embedded AI / edge-AI devices: smart cameras, vision-based sensors, AIoT gateways, inference at the edge rather than cloud.
• Robotics / drones / autonomous sensors: moderate-performance robotics, onboard vision, sensor processing, control logic + some AI inference.
• Compact integrated products: devices where you design a bespoke carrier board to embed the module — for industrial automation, inspection tools, embedded network video recorders, smart devices.
• AI / vision / multimedia at low-power & small size: thanks to GPU + video encode/decode + LPDDR4 + eMMC, it’s suitable for tasks like real-time video analytics, object detection, AI-enabled camera systems, small-form-factor systems.
• Cost-sensitive and power-sensitive deployments: Compared to heavier AI modules, Jetson Nano provides a good balance of cost, size, and reasonable compute power.
“The Jetson Nano module delivers up to 472 GFLOPS of accelerated computing, and can run many modern neural networks in parallel — enough to process data from multiple high-resolution sensors while consuming as little as ~5 W.”
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