Randomized trial evaluates fine-tuned specialist ensembles against monolithic models, indicating potential efficiency gains.
The dominant paradigm in large language model deployment favors monolithic architectures: single large models (70B–400B+ parameters) trained on diverse data and tasked with general-purpose reasoning. This paper presents an alternative approach developed at Hayula Labs: ensembles of fine-tuned 7-8B parameter specialist models, each optimized for a narrow domain, coordinated by a lightweight routing layer.
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Yahya Saqban (2026) studied this question.
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