Quantization and Hardware Effects on Small Language Model Throughput in SLM-Bench

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does the inference throughput of small language models on SLM-Bench tasks vary across different quantization levels and hardware accelerators. Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, predictive AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference throughput of small language models on SLM-Bench tasks vary across different quantization levels and hardware accelerators?

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