LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models
arXiv:2606.00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource-constrained devices. Low-rank decomposition has emerged as a promising compression technique, yet existing methods often optimize local matrix reconstruction error, rely on uniform or heuristic rank allocation, and focus mainly on attention projections while leaving feed-forward networks underexplored. In this paper, we propose~\textit{LASER} (\textbf{L}oss-\textbf{A}w
The proliferation of advanced vision-language models necessitates more efficient deployment methods, driving research into sophisticated compression techniques that maintain performance.
This development addresses a critical bottleneck in VLM adoption, enabling wider deployment on diverse hardware and potentially democratizing access to powerful multimodal AI.
Current methods for VLM compression are often suboptimal; LASER proposes a more effective approach by optimizing for overall loss and intelligently allocating rank.
- · AI developers
- · Edge computing device manufacturers
- · Users of multimodal AI applications
- · Resource-constrained regions
- · Developers relying solely on brute-force compute for VLM deployment
More efficient and compact vision-language models become deployable on a wider range of devices.
Increased accessibility of advanced VLMs could accelerate innovation in practical AI applications across various sectors.
Dramatically lower computational and energy requirements for VLMs could alleviate pressure on compute and power infrastructure, potentially influencing AI infrastructure development strategies.
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Read at arXiv cs.LG