<p>This position paper argues that small language models (SLMs), defined as those runnable on consumer-grade hardware, are not only sufficient but superior for many agentic AI applications, especially when tasks are narrow, repetitive, or tool-oriented. The authors propose that shifting from LLM-first to SLM-first architectures will yield major gains in efficiency, modularity, and sustainability.</p>
<p><a href="https://arxiv.org/abs/2506.02153" target="_blank" rel="noopener nofollow">https://arxiv.org/abs/2506.02153</a></p>


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A position paper argues that small language models (SLMs) running on consumer hardware are superior to large language models for many AI agent applications, particularly for narrow, repetitive, or tool-oriented tasks. The research proposes that SLM-first architectures offer significant advantages in efficiency, modularity, and sustainability compared to traditional LLM-based approaches.

Small Language Models are the Future of Agentic AI