The number of parameters in a grouped convolution will most likely differ, e.g. in the example posted above you see that each kernel has 20 input channels due to the 5 groups instead of 100 as in a vanilla convolution.
I’m not sure you can generalize the speed, since depending on e.g. the kernel size different algorithms might be used by cuDNN, so that even model with more operations (on paper) might run faster than other optimized models.
Basically the same applies to the accuracy, as the model architecture is problem dependent, so you would just have to try out different approaches or stick to a good paper claiming good results using grouped/non-grouped convolutions.