Distilled CodeLlama-33B on Edge Devices: Latency-Accuracy Trade-offs in Vulnerability Detection

This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and vulnerability detection accuracy when optimizing CodeLlama-33B for deployment on edge devices using model distillation techniques. To help MLOps engineers decide which operator to use in which deployment scenario, this study aims to empirically assess the accuracy vs latency trade-off of white-box (training-based) and black-box operators (non-training-based) and their combinations in an Edge AI setup. We. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the trade-off between inference latency and vulnerability detection accuracy when optimizing CodeLlama-33B for deployment on edge devices using model distillation techniques?

Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.