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PACE framework accelerates VLM inference by optimizing vision encoder and LLM

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PACE, a novel training-free framework designed to accelerate the inference speed of Vision-Language Models (VLMs). PACE addresses limitations in existing methods by optimizing both the vision encoder and the LLM through a unified Condense-and-Extract paradigm. The framework includes an Adaptive Pixel Compressor to downsample redundant visual inputs before encoding and a Dynamic Dual-Attention Extractor to selectively retain task-critical visual tokens. When integrated with Qwen2.5-VL-7B, PACE achieved a 3.1x speedup in time to first token while maintaining 93.8% of its original performance by using only 10% of the visual tokens. AI

IMPACT Accelerates VLM inference speed and reduces computational cost, potentially enabling wider adoption and real-time applications.

RANK_REASON The item is a research paper detailing a new method for accelerating Vision-Language Model inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PACE framework accelerates VLM inference by optimizing vision encoder and LLM

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The item is a research paper detailing a new method for accelerating Vision-Language Model inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junjie Liu, Shengyuan Ye, Xu Chen ·

    PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

    arXiv:2608.27206v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation of visual tokens. Existing visual token pruning methods exhibit two fundamental…

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