Inference tasks on graphical models (in particular, Markov random networks) include computing modes and marginals of Gibbs ditribution. Algorithmically, these tasks lead to labeling problems. I want to discuss some suboptimal approaches to these problems for large and sparse instances, occuring e.g. in machine vision and pattern recognition.