title: Small Language Models are the Future of Agentic AI
summary: Large language models (LLMs) are often praised for exhibiting near-human
  performance on a wide range of tasks and valued for their ability to hold a
  general conversation. The rise of agentic AI systems is, however, ushering in a
  mass of applications in which language models perform a small number of
  specialized tasks repetitively and with little variation.
    Here we lay out the position that small language models (SLMs) are
  sufficiently powerful, inherently more suitable, and necessarily more
  economical for many invocations in agentic systems, and are therefore the
  future of agentic AI. Our argumentation is grounded in the current level of
  capabilities exhibited by SLMs, the common architectures of agentic systems,
  and the economy of LM deployment. We further argue that in situations where
  general-purpose conversational abilities are essential, heterogeneous agentic
  systems (i.e., agents invoking multiple different models) are the natural
  choice. We discuss the potential barriers for the adoption of SLMs in agentic
  systems and outline a general LLM-to-SLM agent conversion algorithm.
    Our position, formulated as a value statement, highlights the significance of
  the operational and economic impact even a partial shift from LLMs to SLMs is
  to have on the AI agent industry. We aim to stimulate the discussion on the
  effective use of AI resources and hope to advance the efforts to lower the
  costs of AI of the present day. Calling for both contributions to and critique
  of our position, we commit to publishing all such correspondence at
  https://research.nvidia.com/labs/lpr/slm-agents.
paper_authors._id: 6841c780655161e7f1207108
paper_authors.name: Peter Belcak
paper_authors._id: 6841c780655161e7f1207109
paper_authors.name: Greg Heinrich
paper_authors._id: 6841c780655161e7f120710a
paper_authors.name: Shizhe Diao
paper_authors._id: 6841c780655161e7f120710b
paper_authors.name: Yonggan Fu
paper_authors._id: 6841c780655161e7f120710c
paper_authors.name: Xin Dong
paper_authors._id: 6841c780655161e7f120710d
paper_authors.name: Saurav Muralidharan
paper_authors._id: 6841c780655161e7f120710e
paper_authors.name: Yingyan Celine Lin
paper_authors._id: 6841c780655161e7f120710f
paper_authors.name: Pavlo Molchanov
paper_title: Small Language Models are the Future of Agentic AI
paper_summary: Large language models (LLMs) are often praised for exhibiting near-human
  performance on a wide range of tasks and valued for their ability to hold a
  general conversation. The rise of agentic AI systems is, however, ushering in a
  mass of applications in which language models perform a small number of
  specialized tasks repetitively and with little variation.
    Here we lay out the position that small language models (SLMs) are
  sufficiently powerful, inherently more suitable, and necessarily more
  economical for many invocations in agentic systems, and are therefore the
  future of agentic AI. Our argumentation is grounded in the current level of
  capabilities exhibited by SLMs, the common architectures of agentic systems,
  and the economy of LM deployment. We further argue that in situations where
  general-purpose conversational abilities are essential, heterogeneous agentic
  systems (i.e., agents invoking multiple different models) are the natural
  choice. We discuss the potential barriers for the adoption of SLMs in agentic
  systems and outline a general LLM-to-SLM agent conversion algorithm.
    Our position, formulated as a value statement, highlights the significance of
  the operational and economic impact even a partial shift from LLMs to SLMs is
  to have on the AI agent industry. We aim to stimulate the discussion on the
  effective use of AI resources and hope to advance the efforts to lower the
  costs of AI of the present day. Calling for both contributions to and critique
  of our position, we commit to publishing all such correspondence at
  https://research.nvidia.com/labs/lpr/slm-agents.
paper_ai_summary: Small language models are argued to be more suitable, powerful, and economical for specialized, repetitive tasks in agentic systems, with heterogeneous systems using multiple models where general conversational abilities are needed.
paper_ai_keywords: large language models (LLMs) small language models (SLMs) agentic AI systems heterogeneous agentic systems LLM-to-SLM agent conversion