Typical implementations of genomic prediction utilize Markov chain Monte Carlo (MCMC) sampling to estimate effects. Metropolis-Hastings (MH) is a commonly-used algorithm. We considered three different Gibbs samplers to speed up BayesB, a commonly-used model for genomic prediction. These differ in the manner they sample the marker effect, the locus-specific variance and the indicator variable. They are a single-site Gibbs Sampler, a blocking Gibbs Sampler and a Gibbs Sampler with pseudo prior. These three versions of BayesB are about twice as fast as the one using a MH algorithm.