Quantized InternVL Models: Inference Latency and Memory Trade-offs on Edge Devices

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the efficiency tradeoff between 7B and 13B InternVL models in terms of inference latency and memory usage when deployed on edge devices with quantized weights. Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. This makes them highly appropriate for systems with limited resources and low power capacity. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the efficiency tradeoff between 7B and 13B InternVL models in terms of inference latency and memory usage when deployed on edge devices with quantized weights?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance