If users get a fixed set/numbers of Paramus for LoRa - say 1-5% - it doesn't sound that crazy.

The crazy part is probably loading, running a model pr users - but if the untrained (unspecialized?) model can somehow stay resident - might be possible to load adjusted weights on demand?

I suppose each run now needs a different context pr user/customer anyway, for cache hit rate, if nothing else?