This article details a systematic study of optimal architectures for small language models with approximately 70 million parameters. Researchers discovered that model performance follows a binary tier system determined by a specific hidden dimension threshold or a "Goldilocks" depth of 32 layers. While most traditional architectures performed similarly at this scale, diffusion models like the new Dhara-70M emerged as superior for high-speed throughput and factual accuracy. The study also highlights that converting existing models to diffusion architectures is ten times more efficient than training them from scratch. Ultimately, the findings suggest that model shape and inference style are more critical than specific family designs for small-scale efficiency.

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