WEIZHU CHEN
TECH & AI
WEIZHU CHEN
TITLE: TECHNICAL FELLOW, CVP, GEN AI
COMPANY: MICROSOFT INDUSTRY: TECHNOLOGY
Weizhu has spent nearly two decades at Microsoft, working primarily on AI. He gained his PhD in Computer Science from The Hong Kong University of Science and Technology.
What makes SLMs more sustainable than LLMs? The lower energy footprint of SLMs is a primary driver of their adoption in sustainability strategies. As each AI model scales down in size, its training and inferencing energy requirements plummet, allowing organisations to meet emissions targets while scaling their use of automation and intelligent services.
Unlike LLMs, SLMs can be deployed directly on edge devices or minimal on-premises infrastructure, further diminishing dependence on energyintensive centralised data centers.
Green AI is the movement to prioritise efficiency, environmental responsibility and inclusivity in AI development – and SLMs naturally align with these goals.
The cost efficiency of SLMs is compelling for many companies boosting AI infastructure, particularly for organisations seeking to democratise AI or deploy it at scale where cloud costs are a constraint. Smaller models mean lower infrastructure expenses, faster finetuning and minimal GPU requirements.
SLMs are not just greener and cheaper – they are also easier to audit and control. Their simpler structures make it possible for data scientists and compliance teams to explain, debug and mitigate risks faster than with the opaque, massive architectures of LLMs.
The same transparency is proving particularly valuable in regulated sectors such as healthcare and banking, where rapid model explainability is a legal necessity.
SLMs deliver operational flexibility central to modern IT strategies.
sustainabilitymag. com 169