Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference
arXiv:2606.31903v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acceleration methods usually remove visual tokens or skip visual-token updates in entire layers, but these coarse strategies may discard fine-grained evidence or suppress useful operators together with redundant ones. In this paper, we study visual-token computation from an answer-observable perspective and find that late visual-token updates can remain large while having little effect on answer-token
The increasing complexity of multimodal large language models (MLLMs) and the growing demand for their efficient deployment necessitate innovation in visual processing optimization.
This development addresses a critical bottleneck in MLLM inference, potentially leading to more widespread and cost-effective deployment of advanced AI applications.
The proposed operator-level visual skipping method allows for more granular and efficient MLLM inference compared to existing coarse-grained strategies.
- · AI model developers
- · Cloud providers
- · AI application users
- · Inefficient MLLM architectures
- · Compute-resource constrained users
More efficient MLLM inference reduces computational costs and accelerates development cycles.
Improved efficiency could enable new applications of MLLMs that were previously too computationally expensive, expanding their market reach.
Drives further innovation in hardware and software co-design for optimized MLLM processing, impacting the compute supply chain.
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Read at arXiv cs.AI