Quantization Effects on DeepSeek R1 and Codestral Code Generation Efficiency

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of quantization techniques (e.g., 4-bit, 8-bit) on the inference efficiency (throughput, latency) of DeepSeek R1 when generating optimized Python code suggestions compared to. The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM service providers. To efficiently use GPU resources and boost throughput, batching multiple requests. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of quantization techniques (e.g., 4-bit, 8-bit) on the inference efficiency (throughput, latency) of DeepSeek R1 when generating optimized Python code suggestions compared to Codestral?

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