Scaling Inference Latency of SecLM Variants on Edge Devices vs. Cloud GPUs

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the inference latency of SecLM variants scale with model size when processing multimodal inputs on edge devices compared to cloud GPUs. With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile. 3 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference latency of SecLM variants scale with model size when processing multimodal inputs on edge devices compared to cloud GPUs?

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