How to reduce pipeline footprint in scikit-learn?
I don't know if it is still an open question, anyway.

What is painful when using TFidf, it is the size of the ouput sparse matrix. II think it is your problem here. In order to pass this, you should limit the amount of word you want to treat. I didn't try with your case but you should try CountVectorizer before TfidfTransformer or directly TfidfVectorizer.
In CountVectorizer and TfidfVectorizer from sklearn, you can specify the minimum and maximum number of document any term can be found in (min_df and max_df).
This will reduce the size of the output A.

Hope it helps a bit