Split Computing Partitioning Strategies and Throughput in Llama3-70B vs. Codestral-34B for Code Generation

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the effect of model partitioning strategies in split computing on the throughput of Llama3-70B versus Codestral-34B for code generation tasks on HumanEval-hard. We introduce SIMCOPILOT, a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants. Targeting both completion (finishing incomplete methods or code blocks) and infill tasks (filling missing segments within existing. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the effect of model partitioning strategies in split computing on the throughput of Llama3-70B versus Codestral-34B for code generation tasks on HumanEval-hard?

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