Keras' 'normal' LSTM uses the GPU?
Not sure if you figured this out, but I've been looking into it recently, and this is what I've found:

Is the 'normal' LSTM assisted by GPU?
is the normal LSTM supposed to be faster running on GPU or CPU?

Like @pcko1 said, LSTM is assisted by GPU if you have tensorflow-gpu installed, but it does not necessarily run faster on a GPU. In my case, it actually slowed it down by ~2x, because the LSTM is relatively small and the amount of copying between CPU and GPU made the training slower. I think with a larger network, it would speed things up. I also found that LSTM only used ~25% of the GPU, while CuDNNLSTM used ~35% of the GPU, but haven't done a thorough investigation to figure out where the difference comes from.

how are LSTM and CuDNNLSTM different?

This github issue talks about how to convert CuDNNLSTM layers to LSTM layers. I found it pretty illuminating in how CuDNNLSTM has 2x weights/biases as LSTM, and how to convert from one to the other.