I have seen a common "rule of thumb" for VIF: below 10 is good, above 10 is bad. This rule is unfortunately useless and should not be followed.
In general, a tolerable VIF depends on the relative size of the parameter estimate and the response variance. If your response changes are huge and your variance is small, you can tolerate very large VIF. On the other hand, if your response changes are small and your variance is large, then a small VIF might be too much.
I wonder if you could use another regression method. For example, PLS is very successful where the X are the domain of some spectra, like wavelength or molecular weight. These X are highly correlated. The PLS regression model exploits this information instead of penalizing you with collinearity.
If you have JMP Pro, then you could also treat the X as functional data and save the functional principal components. These FPCs would be used in place of X in your regression model of Y.
What do you think?