Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects

Researchers have identified efficiency bottlenecks in large vision-language models due to visual token dominance, affecting high-resolution feature extraction and memory bandwidth constraints. This study offers a structured analysis of techniques across the inference lifecycle, revealing how upstream decisions impact downstream performance and outlining future optimization directions like hybrid compression and modality-aware decoding.

Read the full article at arXiv cs.CL (NLP)

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