(LLM Application-NVIDIA) Small Language Models: The Future of Agentic AI
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(LLM Application-NVIDIA) Small Language Models: The Future of Agentic AI
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The provided text argues that small language models (SLMs) are the future of agentic AI, positioning them as more economical and operationally suitable than large language models (LLMs) for the majority of tasks within AI agents. While LLMs excel at general conversations, agentic systems frequently involve repetitive, specialised tasks where SLMs offer advantages like lower latency, reduced computational requirements, and significant cost savings. The authors propose a shift to heterogeneous systems, where SLMs handle routine functions and LLMs are used sparingly for complex reasoning. The document also addresses common barriers to SLM adoption, such as existing infrastructure investments and popular misconceptions, and outlines a conversion algorithm for migrating agentic applications from LLMs to SLMs.
Link: https://arxiv.org/pdf/2506.02153