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murgindrag parent
Not for anything I'd wanna use it for. USB 2 makes it hard to get data in and out. Video is 720p single stream. I'm grasping at straws here.

Seems like a total loser compared to the NVidia Jetson line. It does have a few more TOPs than the Nano, I guess? And it costs a bit less than the Xavier series?


Jetson Nano doesn't have dedicated AI hardware... so 472 GFLOPs that you can use there pretty much. Same applies to TX2.

NV hardware with dedicated AI starts with Xavier, with the NX having 21 TOPs at $399 and 32 TOPs for the AGX at $699... that might leave a space for Google Edge TPUs.

shorts_theory
I don’t think the Jetson nano even has a DL accelerator, so I could see the Google SBC achieving much better INT8 inference performance. The Jetson has a CUDA capable GPU, so the comparison is kind of apples and oranges.
murgindrag OP
The Google unit will definitely achieve better DL performance than the Nano. But the set of applications where:

* The Nano's 0.5 TFLOP of CUDA is insufficient; AND

* Google's 4 TOP of DL performance IS sufficient; BUT

* You're not bottlenecked by things like USB2; AND

* You can't afford a higher-end Jetson with dedicated ML

Well, let me just say I can't think of one on things I've actually done. I could come up with ones in abstract, but it seems like Google is aiming for 1% of the market at best. With 1% of the market, NVidia will win on community, tooling, R&D budget for v2, etc., so in the end, that means 0.1% of the market.

And that's if the two businesses were starting on even footing. They're not. Google has a horrible reputation for leaving customers high-and-dry, while NVidia's reputation is pretty good for B2B. Even if the boards were equivalent, most would pick NVidia based on that alone. Plus, NVidia is coming in with established users; they have a first-comer advantaged.

Getting late to the game with a generally inferior product with backing from a less reliable company? It seems like a loser to me.

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