Machine Unlearning is a valuable ability of LLMs, enabling the removal of unsafe, outdated, or private information. Existing unlearning methods, however, are often evaluated under the assumption that all facts are equally challenging to forget. Controllable knowledge removal is essential for reliable NLP systems. In this paper, we investigate whether fact popularity influences the efficiency of LLM unlearning. To answer this question, we build **UNLamb** benchmark designed to systematically investigate this relationship. It consists of 11.6k question-answer pairs derived from real-world knowledge in Wikidata, explicitly partitioned into rare and popular facts. Using this benchmark, we perform a comprehensive evaluation of state-of-the-art unlearning algorithms on a set of models of different sizes. We conduct a comprehensive analysis of four unlearning methods across three validation sets and two LLMs. We show that larger models struggle more to forget popular entities, often damaging related knowledge in the process. In contrast, it is much easier to remove rare facts without side effects.