But why? For instance, while the model is training, I am able to load another model from a jupyter kernel to see some predictions which takes approximately another 1.3 GB of the GPU memory. At the end when I look at the GPU situation, I saw that 7.7 GB of GPU memory was being used while the training and testing processes were running together. Because of this I think that in principle, I should be able to allocate more memory to training process.