MQuant introduces a breakthrough approach to significantly reduce the computational demands of multimodal large language models without sacrificing performance.

• Addresses the unique challenges of quantizing vision-language models
• Achieves up to 76% reduction in model size and 2.3x faster inference
• Proposes a novel calibration process specifically designed for multimodal architectures
• Maintains comparable performance to full-precision models across various benchmarks

This research enables practical deployment of powerful multimodal AI systems on resource-constrained devices, making advanced vision-language capabilities more accessible for real-world applications.

MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization