arXiv AI August 26, 2026 By Lyuke Wang, Zhuo Li, Guangxu Zhu

VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference

VisCache introduces a two-stage, plug‑and‑play framework for pruning visual key‑value caches in Vision Large Language Models without retraining. The first stage filters out temporally redundant keyframes, while the second stage, PruneKV, applies a parabolic layer‑wise budget and asymmetric update to selectively prune keys and fuse values, preserving essential context. Experiments show up to 2.35× speedup and significant memory savings with only 19–28% of the original cache retained, outperforming existing baselines.

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Related stories

AVIS: Adaptive Test-Time Scaling for Vision-Language Models

STaR-KV: Spatio-Temporal Adaptive Re-weighting for KV Cache Compression in GUI Vision-Language Models

KARA: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models