Both the Jetson Nano and Raspberry Pi 4 are new for 2019, and both have powerful specs. This review compares the two to see which is useful for what kind of project. Obviously the Nano costs a lot more, and has a huge edge in GPU as meant for AI projects.
Jetson Nano compute module inserted into carrier board - $99 - also with 4GB of memory
For full details see the attached video. The screen shots here were developed by the Explaining Video presenter, and there is a lot more details inside the video.
He notes at the end that the Jeston Nano team is evaluating how to integrate ffmpeg support with the Nano GPU. If that is implemented then tools like Plex or Kodi would scream on the Jetson Nano.
The video does not attempt to compare performance with AI or Machine Learning tasks. Of course the Jetson Nano is primarily positioned as a Machine Learning workhorse.
CPU's are relatively similar in powerMemory is the sameGPU is where the boards are incredibly differentBoth boards support dual displays, and both offer hardware decoding of H.264 streams at up to 4K 60 fpsThe Octane Benchmark measures JavaScript performance in a Chromium web browser. The Raspberry Pi 4B edges out the Nano by a good margin, but more importantly both demolish the performance of the Raspberry Pi 3. In this benchmark the hdparm program is used to read from disk with no caching. The USB3 SSD is a high performance SSD drive connected through the USB port, while the MicroSD test is the SD card used to boot the machine. Read performance for both the 4B and the Nano smash the performance of the Raspberry Pi 3. And here the Nano has the edge over the Pi 4B.In this test KDEnlive was used to render the same video snippet on both boards. In both cases the CPU got quite busy during rendering. It was noted that on the Jetson Nano, KDEnlive worked much better than on the Raspberry Pi 4. you can see the render time was hugely improved.
Raspberry Pi 4B vs Jetson Nano
About the Author(s)
David Herron:
David Herron is a writer and software engineer focusing on the wise use of technology. He is especially interested in clean energy technologies like solar power, wind power, and electric cars. David worked for nearly 30 years in Silicon Valley on software ranging from electronic mail systems, to video streaming, to the Java programming language, and has published several books on Node.js programming and electric vehicles.
