Can a CNN have a different number of convolutional layers and kernel and what does it mean?

So if I have $$3$$ RGB channels, $$6$$ convolutional layers and $$4$$ kernels, does this mean that each kernel does a convolution on each channel and so the input for the next convolution will be $$3 \times 4=12$$ channels? Or those outputs are just stacked on each other (summed) and the input to the next neural network is still 3 channels?

Edit: I am pretty sure that the input for the next convolution would still be $$3$$, but why is that? What is the operation performed?