why Nvidia A100 GPUs slower than RTX 3090 GPUs?

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Hello, we have RTX3090 GPU and A100 GPU.
Using the Matlab Deep Learning Toolbox Model for ResNet-50 Network, we found that the A100 was 20% slower than the RTX 3090 when learning from the ResNet50 model.
The questions are as follows.
1. I heard that the speed of A100 and 3090 is different because there is a difference between the number of CUDA cores and the number of Tensor cores, so can only use Cuda cores for Matlab?
If you can use it, I would appreciate it if you could send me a link if you have an example site using Tensor core.
2. You can specify single inference, double inference, and half inference methods when learning GPU. I heard that Matlab uses double inference automatically, so please check if it is the correct answer.
Thank you.

Accepted Answer

David Willingham
David Willingham on 13 May 2022
  2 Comments
Joss Knight
Joss Knight on 16 May 2022
It is possible to train models in double precision, using model functions, or using a dlnetwork and converting its weights to double precision before training.
However, I don't believe this is what you want. You won't get a speedup over the RTX 3090 training in single precision, it will still be considerably slower.

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