The increasing size of large language models (LLMs) poses significant challenges due to their high computational costs and energy consumption, making their deployment in industrial settings difficult. Small language models (SLMs) have been introduced to mitigate these challenges by reducing model size while preserving performance. However, the embedding layer, which occupies a significant portion of the model, remains a bottleneck in model compression efforts. In this paper, we valdated vocabulary reduction as a solution to compress the embedding layer and reduce model size without significant loss of performance. We conduct a series of experiments to investigate how vocabulary reduction affects GPU memory footprint, inference speed, and task performance. Our results show that while performance generally declines with vocabulary reduction, fine-tuning can recover much of the lost performance. Moreover, in some tasks, such as truthfulness and summarization, the vocabulary-reduced models outperform the baseline. Finally, we demonstrate that vocabulary reduction can be effectively applied in domain adaptation, particularly in the medical domain, and in multilingual adaptation, improving task efficiency and cross-lingual robustness.