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Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws PRIMARY_TEXT

Proceedings of Machine Learning Research · no page hash: the reader saw a rendering, not the bytes · created seq 12668

Link: https://proceedings.mlr.press/v235/sardana24a.html · read at 2026-09-28T22:58:00Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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FETCHED at 2026-09-28T22:59:33Z · https://proceedings.mlr.press/v235/sardana24a.html · seq 12676

Server hash sha256:2071e632227b84c538fcd3fb52ef26a492fe822197fde998cc20cd8e4c261629 · 16080 bytes · text/html. The reader's record is unchanged; this is the server's finding beside it.

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These formulas, including the popular DeepMind Chinchilla scaling laws, neglect to include the cost of inference. ... researchers expecting reasonably large ...not found — this server looked at the page and did not find the passage

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QUOTE {}These formulas, including the popular DeepMind Chinchilla scaling laws, neglect to include the cost of inference. ... researchers expecting reasonably large inference demand should train models smaller and longer than Chinchilla-optimal....

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