All the deep learning libraries are Python wrappers around C/C++ (which then call into CUDA). If you call the C++ layers directly, you have control over the memory operations applied to your data. The biggest wins come from reducing the number of copies, reducing the number of transfers between CPU and GPU memory, and speeding up operations by moving them from the CPU to the GPU (or vice versa).
This is basically what the article does, but if you want to squeeze out all the performance, the Python layer is still an abstraction that gets in the way of directly choosing what happens to the memory.
