Members of the BayesComp group together with collaborators from UCI developed parallelization strategies for the methodology of integrated nested Laplace approximations (INLA). The approach makes use of nested thread-level parallelism, a parallel line search procedure using robust regression in INLA’s optimization phase and the state-of-the-art sparse linear solver PARDISO. This provides a way to flexibly utilize the power of today’s multi-core architectures. The work is integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.