Multimodal anomaly detection (MAD) enhances industrial inspection by leveraging complementary 2D and 3D data. However, existing methods struggle in few-shot scenarios due to limited data and modality gaps. While current approaches either fuse multimodal features or align cross-modal representations, they often suffer from high false positive rates and fail to detect subtle defects, especially when training data is scarce. To address these challenges, we propose the first few-shot MAD method FIND, a novel dual-student framework that synergistically integrates intra-modal reverse distillation and cross-modal distillation. FIND employs modality-specific teachers and two collaborative students: an intra-modal student for fine-grained anomaly localization via reverse distillation, and a cross-modal student that captures inter-modal correspondences to detect inconsistencies. Extensive experiments on MVTec-3D-AD and Eyecandies show that FIND outperforms state-of-the-art methods in both full-shot and few-shot settings. Ablation studies validate the complementary roles of intra- and cross-modal distillation. Our work significantly advances MAD robustness in data-scarce industrial applications.