LV-XAttn introduces a novel distributed cross-attention mechanism that efficiently processes large visual inputs across multiple GPUs with minimal communication overhead.

• Reduces memory requirements for processing videos and large image sets in multimodal models
• Minimizes data transfer between GPUs through strategic token distribution
• Maintains model performance while enabling processing of significantly larger visual inputs
• Addresses a critical bottleneck in scaling multimodal applications to complex visual understanding tasks

This research enables more efficient deployment of multimodal AI systems across industries where processing lengthy videos or numerous images is essential, from content creation to visual analysis platforms.

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models