Timeseries VAR vs VARMA model: issue in time to fit model
It's because 80,000 samples is probably too much training data. Usually, statistical forecasting models are not trained on the entire training set like ML models. Try reducing your training data significantly, and incorporate only a certain number of seasons as your training data. 

Example:
If your data is hourly, one season can be 24 samples. Then, the training data can span 14 seasons - 24*14 samples.