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Equation 29 · Dense, Sparse, and Distilled: Comparing Approaches to Frontier Model Capacity

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QQ

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QQ

Symbol Q

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with Q requests over the model’s life. When Q is small — a research artefact, an internal tool — the training term dominates and compute-optimal allocation is the right target. When Q is very large, the second term dominates by orders of magnitude, and every technique that reduces cˉinf\bar{c}_{\mathrm{inf}} pays back continuously while every technique that reduces CtrainC_{\mathrm{train}} pays back once.

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