I’m training a LSTM with a very large corpus. The LSTM has a pre-trained matrix of embeddings with 120000 words, obtained with word2vec. The training set is composed by 900000 training phrases of length 30 each and the validation has 270000 samples. The problem is that when I run this on Google Colab with the maximum RAM and the powerful GPU I get CUDA out of memory. I reduced the batch size to 128 but i think that most of the memory is occupied by the network and vocabulary, hence I’m asking if there are some tricks when you work with very large datasets and networks.