Jetson Nano
Jetson Nano is a compact embedded AI computing development board produced by NVIDIA, targeting edge computing, robotics, and intelligent camera applications. The device features NVIDIA GPU architecture, supports parallel AI inference, and can run deep learning frameworks such as TensorFlow and PyTorch under low-power conditions. Its compact form factor and relatively low cost have made it widely adopted in research, education, and embedded AI prototyping.
Core Facts
Timeline (last 90 days)
Single-board computers such as the Raspberry Pi and Jetson Nano integrate CPU, GPU, and even dedicated AI acceleration units onto a credit-card-sized board, with prices ranging from tens to hundreds of dollars.
Edge computing modules such as the Raspberry Pi and Jetson Nano can be deployed directly alongside production lines to perform real-time inference.
The Jetson Nano has a compute performance of 472 GFLOPS, and the Jetson AGX Orin has a compute performance of 275 TOPS INT8.
The older Jetson Nano supports only up to JetPack 4.6.x, corresponding to CUDA 10.2, which prevents newer framework versions such as PyTorch 2.x and TensorFlow 2.15+ from being natively compiled and run
For projects that process high-resolution video streams, run larger models (YOLOv8m/l), or require real-time detection above 30 FPS, the Jetson Nano will become a significant bottleneck.
An unoptimized PyTorch model may achieve only 2–3 FPS on the Jetson Nano, which can be improved to 10–25 FPS after TensorRT optimization, an improvement of up to 5–8x
The Jetson Nano running lightweight models such as YOLOv5s or YOLOv8n typically achieves 10–25 FPS after TensorRT optimization
The Jetson Nano was released in 2019
The NVIDIA Jetson Nano has a compute performance of 472 GFLOPS and a power consumption of 5–10 W; the Jetson AGX Orin has a compute performance of 275 TOPS and a power consumption of 15–60 W.
All Facts (9)
The NVIDIA Jetson Nano has a compute performance of 472 GFLOPS and a power consumption of 5–10 W; the Jetson AGX Orin has a compute performance of 275 TOPS and a power consumption of 15–60 W.
65%UnverifiedSingle-board computers such as the Raspberry Pi and Jetson Nano integrate CPU, GPU, and even dedicated AI acceleration units onto a credit-card-sized board, with prices ranging from tens to hundreds of dollars.
50%UnverifiedEdge computing modules such as the Raspberry Pi and Jetson Nano can be deployed directly alongside production lines to perform real-time inference.
50%UnverifiedThe Jetson Nano has a compute performance of 472 GFLOPS, and the Jetson AGX Orin has a compute performance of 275 TOPS INT8.
50%UnverifiedAn unoptimized PyTorch model may achieve only 2–3 FPS on the Jetson Nano, which can be improved to 10–25 FPS after TensorRT optimization, an improvement of up to 5–8x
50%UnverifiedThe Jetson Nano was released in 2019
50%UnverifiedThe Jetson Nano running lightweight models such as YOLOv5s or YOLOv8n typically achieves 10–25 FPS after TensorRT optimization
50%UnverifiedFor projects that process high-resolution video streams, run larger models (YOLOv8m/l), or require real-time detection above 30 FPS, the Jetson Nano will become a significant bottleneck.
50%UnverifiedThe older Jetson Nano supports only up to JetPack 4.6.x, corresponding to CUDA 10.2, which prevents newer framework versions such as PyTorch 2.x and TensorFlow 2.15+ from being natively compiled and run
50%