The difference is the same, whether you have 10k series representing the same variables or 10k series representing different variables decomposes into the same forecasting problem that you can either over-generalize across all time-series, producing worse accuracy at the tails, or train a model for each individual forecast, producing worse accuracy in general due to limited data input.