DeepSeek R1 and Codestral Memory and Latency with IceCache vs. On-GPU KV-Cache

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the memory consumption and latency of DeepSeek R1 and Codestral compare when using IceCache's KV-cache management against traditional on-GPU KV-cache in autoregressive generation tasks with. 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

How does the memory consumption and latency of DeepSeek R1 and Codestral compare when using IceCache's KV-cache management against traditional on-GPU KV-cache in autoregressive generation tasks with sequence lengths up to 16k tokens?

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