Inference Efficiency of Llama-3.1-8B, Mistral-7B, and Qwen3-8B for Code Generation on MBPP

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How do Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B compare in terms of inference efficiency (throughput and latency) when generating code on MBPP under constrained hardware conditions. Romanized Nepali, the Nepali language written in the Latin alphabet, is the dominant medium for informal digital communication in Nepal, yet it remains critically underresourced in the landscape of Large Language Models (LLMs). This study presents a systematic benchmarking of. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B compare in terms of inference efficiency (throughput and latency) when generating code on MBPP under constrained hardware conditions?

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