Small Language Models Are the Future of Agentic AI: Here’s Why

Author(s): MKWriteshere

Originally published on Towards AI.

Why specialized SLMs under 10B parameters are replacing 175B LLMs in production AI agents — with 30x cost savings, better performance, and a proven migration roadmap.

If you’re running AI agents in production, you’re probably overpaying.

The article discusses the advantages of Small Language Models (SLMs) over Large Language Models (LLMs) for running AI agents in production, highlighting that SLMs, which are under 10 billion parameters, can perform specific tasks more efficiently and at a lower cost (10-30 times less). The researchers argue that current AI architectures waste resources, and they present evidence indicating that SLMs can often outperform larger models in specialized tasks. They outline a roadmap for migrating from LLMs to SLMs, addressing economic efficiency, operational flexibility, and adaptability. Furthermore, the authors propose that the future of AI development should focus on modular and efficient systems that utilize the right tools for specific jobs, promoting broader participation and reducing environmental impacts associated with AI technology.

Read the full blog for free on Medium.

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Published via Towards AI