Effective software testing relies on the quality and correctness of test assertions. Recent Large Language Models (LLMs), such as CodeT5+, have shown significant promise in automating assertion generation tasks; however, their substantial computational resource demands limit their practical deployment in common development environments like laptops or local IDEs. To address this challenge, this work explores knowledge distillation to derive smaller, more efficient student models from a larger, pre-trained CodeT5+ teacher. While knowledge distillation has been successfully applied to general code models, its specific application to creating lightweight, locally-deployable models for test assertion generation remains a recognized research gap. Using a dataset that includes assertion input-output pairs and teacher logits, we systematically investigate the impact of different distillation loss components—soft logits loss and hard target losses—on student performance. Our findings demonstrate the practical viability of this approach: a distilled 220M parameter student model can be nearly 3x faster and consume over 40% less memory than its 770M teacher, while retaining approximately 78% of the original’s assertion generation quality as measured by CodeBLEU. These results offer practical insights and a clear pathway for deploying efficient yet effective assertion-generation models suitable for local developer workflows.