Throughput-Accuracy Trade-offs in Code Generation with DeepSeek R1 and Codestral on Constrained Hardware

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the trade-off between throughput and accuracy when deploying DeepSeek R1 and Codestral with IceCache's external vector database-based KV-cache on resource-constrained hardware for code. Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation during autoregressive generation. However, its memory footprint scales linearly with sequence length. 11 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 trade-off between throughput and accuracy when deploying DeepSeek R1 and Codestral with IceCache's external vector database-based KV-cache on resource-constrained hardware for code generation tasks, as measured by HumanEval benchmarks?

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