• Small models, big impact: The future of scaling enterprise AI agents Share In the AI industry, we’ve spent the last 3 years obsessed with scale. • We’ve chased parameter counts into the trillions, believing that “bigger” was the only path to “smarter.” But as the dust settles, a new reality is emerging for the enterprise-size is not the metric that matters, delivering reliable, deterministic outcomes is. • At Red Hat, we’ve always believed that the most powerful technologies are those that are distributed, open, and fit-for-purpose. • Small language models (SLMs) represent that exact shift. • The distinction between SLMs andlarge language models(LLMs) is less important than the architectural role the model serves. • What matters is the functional sovereignty a small model brings to the table.

Article Summaries:

  • In the AI industry, we’ve spent the last 3 years obsessed with scale. We’ve chased parameter counts into the trillions, believing that “bigger” was the only path to “smarter.” But as the dust settles, a new reality is emerging for the enterprise-size is not the metric that matters, delivering reliable, deterministic outcomes is. At Red Hat, we’ve always believed that the most powerful technologies are those that are distributed, open, and fit-for-purpose. Small language models (SLMs) represent that exact shift. The distinction between SLMs and large language models (LLMs) is less important tha

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