I am working on a project [0] to generate 1 billion rows in SQLite under a minute and inserted 100M rows inserts in 33 seconds. First, I generate the rows and insert them in an in-memory database, then flush them to the disk at the end. To flush it to disk it takes only 2 seconds, so 99% of the time is being spent generating and adding rows to the in-memory B Tree.
For Python optimisation, have you tried PyPy? I ran my same code (zero changes) using PyPy, and I got 3.5x better speed.
I published my findings here [1].