Embedded speaker recognition in mobile devices involves a limited amount of computing resources. However , the performance of state-of-the-art systems are usually evaluated without any limitation of material resources. In this paper, we evaluate several downsca-led configurations of the LIA UBM/GMM speaker verification system. The impact of scalability is evaluated in terms of memory resources and computational time. We propose two downscale configurations which allow a good compromise between resource consumption and performance degradation. Experiments performed on the Banca database show that the memory allocation and the computational time decrease of about 70% and 88% when the error rate raises from 3, 48% to 4, 77% or still comparable to the baseline.