# Learning speed of only Dense layers vs a CNN

So I understand, that the main advantage from CNNs are that there are less leanrable parameters and that this should speed up learning. Now I build two models to train on the fashion_mnist dataset. These are the Models I created:

def build_model_seq(input_shape,output_shape):
model = tf.keras.models.Sequential()

opt = tf.keras.optimizers.SGD()

model.compile(  optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

return model

def build_model_cnn(input_shape,output_shape):
model = tf.keras.Sequential()
activation='relu',input_shape=input_shape))
activation='relu'))

opt = tf.keras.optimizers.SGD()

model.compile(  optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

return model


Now If I train these models the fitting time on a single epoch is 8 times larger. This can be seen here:

Why is that? Can someone explain this to me please and after that the overall benefit of CNNs other than they just work better?