Considering the teacher model's computation budget in the fair comparison setup could potentially alter the results significantly. The computation budget of the teacher model directly impacts the quality and quantity of knowledge that can be distilled into the student model. If the teacher model has a significantly larger computation budget, it may have access to more diverse and extensive data during pretraining, leading to a richer knowledge base. In such a scenario, the distillation process could be more effective, resulting in better performance of the student model compared to pretraining from scratch. On the other hand, if the teacher model's computation budget is limited, the distilled knowledge may not be as comprehensive, potentially narrowing the performance gap between No-KD and KD strategies. Therefore, accounting for the teacher model's computation budget in the comparison setup is crucial for a more comprehensive evaluation of the effectiveness of knowledge distillation.