Researchers from Nvidia advocate for the use of small language models (SLMs) over large language models (LLMs) for developing AI agents. They argue that SLMs are more economical, suitable, and efficient for specialized tasks that AI agents will often encounter. The increasing use of SLMs could reduce costs associated with AI implementation and alleviate concerns over computational resource misallocation. The report implies that the traditional reliance on LLMs for all applications is economically inefficient and suggests that heterogeneous systems employing various models could enhance AI capabilities.
SLMs 'are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems,' the report said.
Using LLMs for AI agents can be expensive, and it doesn't always match most use cases for the technology, functionally.
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