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I'm using the deep learning framework Caffe on a Ubuntu 14.04 machine. I compiled CAFE with CPU_ONLY option, i.e. I disabled GPU and CUDA usage. I have an NVidia Quadro K2200 graphics card and CUDA version 5.5.

I would like to know if it is possible to use Caffe with CUDA enabled with my GPU. On NVidia page, it is written that Quadro K2200 has a compute capability of 5.0. Does it mean that I can use it with CUDA versions up to release 5.0? When it is possible to use Caffe with GPU-enabled with Quadro K2200, how can I choose the appropriate CUDA version for that?

chronosynclastic
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    compute capability != CUDA version, see e.g. [this SO post](http://stackoverflow.com/questions/10961476/what-are-the-differences-between-cuda-compute-capabilities) – m.s. Aug 19 '15 at 13:35

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CUDA version is not the same thing as Compute Capability. For one, CUDA is current (7.5 prerelease), while CC is only at 5.2. K2200 supports CC 5.0.

The difference:

CUDA version means the library/toolkit/SDK/etc version. You should always use the highest one available.

Compute Capability is your GPU's capability to perform certain instructions, etc. Every CUDA function has a minimum CC requirement. When you write a CUDA program, it's CC requirement is the maximum of the requirements of all the features you used.

That said, I've no idea what Caffe is, but a quick search shows they require CC of 2.0, so you should be good to go. CC 5.0 is pretty recent, so very few things won't work on it.

mtijanic
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