There has been a lot of new research on the multimodal LLM front, including the latest Llama 3.2 vision models, which employ diverse architectural strategies to integrate various data types like text and images. For instance, The decoder-only method uses a single stack of decoder blocks to process all modalities sequentially. On the other hand, cross-attention methods (for example, used in Llama 3.2) involve separate encoders for different modalities with a cross-attention layer that allows these encoders to interact. This article explains how these different types of multimodal LLMs …