Computing mutual information (MI) of random variables lacks a closed-form in nontrivial models. Variational MI approximations are widely used as flexible estimators for this purpose, but computing them typically requires solving a costly nonconvex optimization. We prove that a widely used class of variational MI estimators can be solved via moment matching operations in place of the numerical optimization methods that are typically required. We show that the same moment matching solution yields variational estimates for so-called "implicit" models that lack a closed form likelihood function. Furthermore, we demonstrate that this moment matching solution has multiple orders of magnitude computational speed up compared to the standard optimization based solutions. We show that theoretical results are supported by numerical evaluation in fully parameterized Gaussian mixture models and a generalized linear model with implicit likelihood due to nuisance variables. We also demonstrate on the implicit simulation-based likelihood SIR epidemiology model, where we avoid costly likelihood free inference and observe many orders of magnitude speedup.