Fast and/or memory efficient classification solutions I'm working on a classification problem with a very large dataset (a little under 1 billion obs) and around 25 predictors.

I'm doing this analysis in R on a VM with 128GB of memory, but am still hitting memory issues when training certain types of models, not to mention having to wait for quite a while for runs to complete. I'm primarily using logistic regression and random forests, and keeping my training dataset to 10% of the overall sample.

What are some solutions (packages, platforms, techniques) I could use to address these memory and/or speed issues? I'm fairly comfortable with Python, so solutions do not have to be R-specific.
LightGBM is the answer you are looking for. It uses less memory than xgboost, catboost and the fastest GBM library. Also it has a RF mode which is faster than sklearn implementation. It works on both R and Python. Also it has some parameters to control memory management.
You can check this benchmark repo.