ABSTRACT.- Random regression models (RRM) combined with single-step genomic best linear unbiased prediction (ssGBLUP) are widely used for genomic evaluations in dairy cattle. This study aimed to efficiently implement RRM with ssGBLUP for national dairy cattle evaluations. Data from the Czech Holstein population were used, including 30 million test-day records for milk yield across 3 lactations. The pedigree included 2.5 million animals, of which 54,000 were genotyped. To enhance model convergence, we used a reduced number of genetic groups by combining groups with few records, and treated them as random. Additionally, the algorithm for proven and young (APY) was applied. Mixed model equations were solved by the preconditioned conjugate gradient method using iteration on data. External information from Interbull was included as deregressed proofs (DRP) of cumulative 305-d multicountry evaluation approach (MACE) breeding values and weighted by effective record contributions (ERC). © 2025 The Authors.