Karyo_Ten t1_iscf3fp wrote on October 14, 2022 at 10:00 PM

Reply to Training speed using 32 vs 64-bit floating point by Troll_of_the_bridge

There is no way you are using 64-bit on the GPU.

All the CuDNN code is 32-bit for the very simple reason that non-Tesla GPUs have between 1/32 to 1/64 FP64 throughput compared to FP32.

See https://www.reddit.com/r/CUDA/comments/iyrhuq/comment/g93reth/

So under the hood your FP64 stuff is converted to FP32 when sent to GPU.

And on Tesla GPUs the ratio is 1/2.

Karyo_Ten t1_iqnb0i2 wrote on October 1, 2022 at 5:26 PM

Reply to Deciding between rtx 2060 laptop and m1 macbook air for deep learning by gokul113

Neither.

Mac M1 for deep learning? No nvidia GPU, no deep learning, and before people pull pitchforks about PyTorch and Tensorflow supporting M1, it's a pain and many ecosystem packages only support Cuda. And recompiling everything is a time sink.

The RTX 2060 is a bit of a bummer when 3060 12GB is a clean upgrade for not much more, 6GB is getting small these days, and you didn't mention the RAM? 16GB minimum just to have our browser, VScode, and Discord/Slack or whatever you use to communicate and then your model.