Hi! There are two batch sizes here: first, the batch size used to partition the two generators and interleave them, and second, the batch size that Prodigy uses to divide the final stream into batches.
The batch_size on combine_models is less relevant here and mostly used for efficiency. In the end, the predict function still just yields (score, example) tuples. The batch_size setting in Prodigy is what decides about how many examples are fetched from the stream at once, how many are sent to the web app and how many are sent back to the server.
See my reply here for more ideas on how you could solve your problem: