Code Llama 34B and 70B Inference Latency and Throughput Under Large Context Windows

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the comparative inference latency and throughput efficiency of 34B versus 70B Code Llama models when generating complex multi-file code solutions under large context window constraints. We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide. 16 claims were extracted from source literature; 14 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the comparative inference latency and throughput efficiency of 34B versus 70B Code Llama models when generating complex multi-file code solutions under large context window constraints?

Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.