I don't understand the claim that you should get a large RAM GPU if you have a large dataset. It's the model and your training procedure that dictates the amoun

I don't understand the claim that you should get a large RAM GPU if you have a large dataset. It's the model and your training procedure that dictates the amount of GPU ram consumed (e.g. fft convolution kernels like fbcunn use a decent amount of RAM compared to CuDNN's fused gemm, larger batch sizes mean more ram for storing activations, etc).