This study investigates how Large Language Models (LLMs), particularly BERT (Devlin et al., 2019) and GPT-2 (Radford et al., 2019), engage in pragmatic inference of scalar implicature, such as some. Two sets of experiments were conducted using cosine similarity and next sentence prediction as experimental methods. The results in experiment 1 showed that, in the absence of context, both models interpret some as pragmatic implicature not all, aligning with human language processing. In experiment 2, in which Question Under Discussion (QUD) was presented as a contextual cue, GPT-2 encountered processing difficulties since a certain type of QUD required pragmatic inference for implicature. Conversely, BERT exhibited consistent performance regardless of types of the QUDs. In theoretical approaches, BERT inherently incorporates pragmatic implicature not all within the term some, adhering to a Default model (Levinson, 2000). In contrast, GPT-2 seems to expend processing effort in inferring pragmatic implicature within context, consistent with a Context-driven model (Sperber and Wilson, 2002).