How meaningful is a high GVIF when calculated for a two-way fixed effects model?

For the model in question (created using feols() from the fixest package), the fixed effects are time, and an individual cross-wave identifier, given that I am using panel data. There are three time periods and around 2,400 cross-wave identifiers. When using the vif() function from the car package in R for a model, the output provides a GVIF statistic statistic > 1,000,000. This effect subsides when removing some independent dummy variables which are coded using as.factor().

However, when testing for multicollinearity in the absence of fixed effects, none of the independent variables in the model exhibit a multicollinearity of greater than 5.

Any help on navigating this would be appreciated.