W4A4 Quantization of Multimodal Models on MMBench in 8GB Edge Deployments

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How do W4A4-quantized multimodal models (e.g., Qwen-VL) perform on the MMBench accuracy compared to their INT8 counterparts when deployed on edge devices with 8GB DRAM. Edge computing environments face unprecedented challenges in deploying large language models due to severe resource constraints, latency requirements, and privacy concerns that traditional cloud-based solutions cannot address. Current approaches struggle with the fundamental. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do W4A4-quantized multimodal models (e.g., Qwen-VL) perform on the MMBench accuracy compared to their INT8 counterparts when deployed on edge devices with 8GB DRAM?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.