Modern speaker verification systems rely on margin-based softmax losses. While functions like CosFace and ArcFace have become standard, they remain largely heuristic in the integration of geometric margins. Recently, the Q-Margin loss was introduced to provide a more principled probabilistic interpretation by embedding margins into a reference measure within the Fenchel-Young loss family. In this work, we rigorously analyze Q-Margin in speaker verification. We provide a proof showing conditions under which CosFace emerges as a special case of Q-Margin. Through extensive experiments across varying model capacities and dataset scales, we demonstrate that Q-Margin is effective in security regimes, achieving significant improvements in FRR at a 0.1% FAR on small-scale datasets. It also provides performance gains when used for fine-tuning. However, we identify a phenomenon in which the sparsity-inducing nature of the loss can lead to gradient starvation in low-capacity models.