The rise of agentic AI is transforming enterprise automation and productivity, focusing on repetitive tasks. Central to this shift are Small Language Models (SLMs), which offer efficient and cost-effective alternatives to Large Language Models (LLMs) for specific applications. In our position paper, “Small Language Models are the Future of Agentic AI,” we advocate for SLMs due to their targeted capabilities, reliability, and significantly lower cost—10x to 30x cheaper than LLMs. While LLMs excel in open-ended dialogues and complex problem-solving, SLMs outperform in specialized tasks like command parsing and structured output generation. By utilizing hybrids of SLMs for routine workloads and LLMs for strategic tasks, enterprises can enhance operational efficiency. Tools like NVIDIA NeMo provide essential resources for organizations to seamlessly transition to SLM-integrated systems. Embracing this heterogeneous architecture fosters innovation, scalability, and cost control, driving the future of automation in business environments.
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