arXiv:2605.29535v1 Announce Type: new Abstract: Vision-Language Models (VLMs) process thousands of visual tokens per image alongside comparatively few text tokens, yet existing compression methods treat both modalities uniformly. We observe that the two modalities have fundamentally different properties: vision tokens are spatially redundant and dominate prefill, while text tokens are causally dependent and accumulate during decoding. Based on this asymmetry, we propose and empirically evaluate AsymVLM, which applies aggressive pruning to vision tokens before prefill using a learned importance
The continuous growth in size and computational demands of large Vision-Language Models (VLMs) necessitates innovative efficiency solutions to make them more practical and accessible.
Improving the efficiency of VLMs addresses a critical bottleneck in AI development, potentially making advanced AI applications cheaper, faster, and more scalable for a wide range of industries.
This development introduces a new method for VLM compression that could significantly reduce inference costs and latency, differentiating it from prior uniform compression techniques.
- · AI developers
- · Cloud providers
- · Companies deploying VLMs
- · AI hardware manufacturers
- · Inefficient VLM architectures
- · High-latency VLM applications
More efficient VLMs allow for broader and more real-time application deployments across various sectors.
Reduced operational costs for AI inference could accelerate the adoption of VLM-powered features in consumer products and enterprise solutions.
The democratization of VLM capabilities due to lower computational barriers could foster a new wave of innovation and lead to unforeseen AI applications.
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Read at arXiv cs.LG