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Towards sustainable and energy-efficient agentic AI via small language models: A comprehensive survey
- Truvace record
- TRV-2026-2535raw data (JSON)
- Version
- v5Sources changed
- First published
- Evidence type
- Commentary, opinion, or narrative review
- Sources
- 5 sourcesPeer-reviewed
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The proliferation of agentic AI is generating an energy consumption crisis that neither incremental hardware improvements nor renewable energy procurement alone can resolve. This survey argues that the most viable pathway toward sustainable autonomous intelligence lies not in making Large Language Models (LLMs) greener, but in fundamentally rethinking the unit of agency: replacing monolithic frontier models with Small Language Models (SLMs) that are purpose-designed for the constrained, repetitive, and specializ…
The gain
A survey of published studies reports that 4-bit quantized models of 1B–7B parameters approach large-model performance on some agentic sub-tasks while cutting energy per token by about one order of magnitude.
What this doesn’t fix
The survey is a conceptual analysis using order-of-magnitude estimates from published data, with no original physical measurements, and the energy savings apply only to the specific model pairs, hardware and workloads each study evaluated.
Sources
5 on file- Computer Science Reviewdoi:10.1016/j.cosrev.2026.101076
- ACM Transactions on Software Engineering and Methodologydoi:10.1145/3747588
- ICT Expressdoi:10.1016/j.icte.2026.10.014
- Artificial Intelligence Reviewdoi:10.1007/s10462-025-11236-4
- Sustainabilitydoi:10.3390/su18021014
Cite this record
v5Truvace Impact Record TRV-2026-2535, v5: “Towards sustainable and energy-efficient agentic AI via small language models: A comprehensive survey.” Truvace, 2026-11-13. /record/TRV-2026-2535 (accessed on the day you copy it). sha256 c7d086cecc429cb0…
Change history
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