Content moderation platforms concentrate resources on English content despite serving predominantly non-English speaking users.Also, given the scarcity of native moderators for low-resource languages, non-native moderators must bridge this gap in moderation tasks such as hate speech moderation.Through a user study, we identify that non-native moderators struggle with understanding culturally-specific knowledge, sentiment, and internet culture in the hate speech.To assist non-native moderators, we present LLM-C3MOD, a human-LLM collaborative pipeline with three steps: (1) RAG-enhanced cultural context annotations; (2) initial LLM-based moderation; and (3) targeted human moderation for cases lacking LLM consensus.Evaluated on Korean hate speech dataset with Indonesian and German participants, our system achieves 78% accuracy (surpassing GPT-4o’s 71% baseline) while reducing human workload by 83.6%.In addition, cultural context annotations improved non-native moderator accuracy from 22% to 61%, with humans notably excelling at nuanced tasks where LLMs struggle.Our findings demonstrate that non-native moderators, when properly supported by LLMs, can effectively contribute to cross-cultural hate speech moderation.