2025-12-11 –, Machine Learning & AI
Small Language Models (SLMs) are designed to deliver high performance with significantly fewer parameters compared to Large Language Models (LLMs). Typically, SLMs range from 100 million to 30 billion parameters, enabling them to operate efficiently on devices with limited computational resources, such as smartphones and embedded systems
The development of SLMs addresses the growing demand for AI solutions that are cost-effective, energy-efficient, and capable of running locally to ensure data privacy and reduce latency. Recent advancements have demonstrated that SLMs can rival or even surpass larger models in specific tasks, thanks to optimized architectures and training methodologies .
A notable example is Google's Gemma 3, a multimodal SLM family with models ranging from 1 to 27 billion parameters. Gemma 3 introduces vision understanding capabilities, supports longer context windows of at least 128K tokens, and employs architectural changes to reduce memory usage . The 27B parameter version of Gemma 3 has achieved competitive performance, ranking among the top 10 models in the LMSys Chatbot Arena with an Elo score of 1339 .
The shift towards SLMs signifies a paradigm change in AI development, focusing on creating models that are not only powerful but also accessible and adaptable to a wide range of applications. As the field evolves, SLMs are poised to play a crucial role in democratizing AI technology.
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