If you have a trillion parameter 8-bit fp network or a trillion parameter 1.5-bit ternary network, based on the scaling in Microsoft's paper the latter will actually perform better.
A lot of the current thinking is that the nodes themselves act as superpositions for a virtualized network in a multidimensional vector space, so precision is fairly arbitrary for the base nodes and it may be that constraining the individual node values actually allows for a less fuzzy virtualized network by the end of the training.
You could still have a very precise 'calculator' feature in the virtual space no matter the underlying parameter precision, and because each parameter is being informed by overlapping virtual features, may even have less unexpected errors and issues with lower precision nodes.