Small Language Models for Resource-Constrained Applied AI

Authors

  • Peter Simmons Author

Keywords:

small language models, efficient AI, quantization, distillation, edge inference

Abstract

This review examines small language models for resource-constrained applied artificial intelligence. The organizing question is which compression and adaptation choices preserve utility, privacy, and calibration under tight compute budgets. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to reporting parameter reduction without measuring end-to-end quality and energy. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in edge, mobile, and embedded AI deployment.

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Journal of Algorithmic Discovery and Applied AI

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This work is licensed under a Creative Commons Attribution 4.0 International License.