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Well, cuDNN doesn't run on AMD cards, so if you want your software to have good performance on all consumers' systems (as well as avoiding being vendor-locked),
by gcp 9y ago
Well, cuDNN doesn't run on AMD cards, so if you want your software to have good performance on all consumers' systems (as well as avoiding being vendor-locked), cuDNN isn't an option. Also, you have to get explicit permission from NVIDIA to redistribute cuDNN (though that reportedly is a formality).
Not being vendor locked in is pretty nice if the competition releases a 22 TFLOPS (fp16) card for only 400 USD. Well, they could have released it, if they had working fp16 support in their driver, sigh.
- mattnewton 9y agoThat's for inference though, the intensive part is training no? Then with a reasonable model (maybe distilled somehow from a larger one through more training) you could even do it off CPU or whatever.
- gcp 9y agofp16 is usable for training and inference, so the Vega would have been good for both. I care about inference performance too, anyway. CPU is only a (much slower) fallback if the OpenCL drivers are broken.