word2vec - reduce RAM consumption when loading model
I am not intimately familiar with the word2vec implementation in gensim but the model, once trained, should basically boil down to a dictionary of (word -> vector) pairs. This functionality is provided by the gensim.models.KeyedVectors class and is independent of the training algorithm used to derive the vectors.
You could extend that class such that it loads the vectors from a database (SQLite for instance) on demand instead of into memory upon creation. 
Probably best if you open an issue on github and start a discussion with the core developers on the matter.
I have about 30 word2vec models. When loading them in a python script each consumes a few GB of RAM so it is impossible to use all of them at once. Is there any way to use the models without loading the complete model into RAM?