QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models

📰 ArXiv cs.AI

QAPruner combines quantization-aware vision token pruning for multimodal large language models to reduce computational costs

advanced Published 6 Apr 2026

Action Steps

Apply Post-Training Quantization (PTQ) to reduce model precision
Use vision token pruning to remove redundant tokens
Integrate QAPruner to jointly optimize PTQ and token pruning for better compression
Evaluate the performance of QAPruner on multimodal large language models

Who Needs to Know This

AI engineers and researchers working on multimodal large language models can benefit from QAPruner to optimize model deployment in resource-constrained settings

Key Insight

💡 QAPruner combines PTQ and vision token pruning to reduce computational costs in multimodal large language models

Key Takeaways

QAPruner combines quantization-aware vision token pruning for multimodal large language models to reduce computational costs

Full Article

Title: QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models

Abstract:
arXiv:2604.02816v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning t

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