In theory, the Gibbs samplers produce streams of samples that eventually converge on the true posterior distribution, while the Variational Bayes (VB) estimator only produces an approximation to the posterior.
as the size of the training data distribution increases the likelihood function and therefore the posterior distribution becomes increasingly peaked, so one would expect this variational approximation to become increasingly accurate.