The Metropolis Hastings Walker (MHW) method (Li et al., 2014) scales well in the number of topics, and uses a collapsed inference algorithm, but it operates in the batch setting, so it is not scalable to large corpora.
the SCVB0 algorithm does not leverage sparsity, and hence requires O(K) operations per word token.