Why AI Labs Abandoned Chinchilla's Training Rule for Cheaper Inference

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The 2022 Chinchilla paper by Hoffmann et al. established that model parameters and training tokens should scale together, with roughly 20 training tokens per parameter representing the compute-optimal ratio. This overturned earlier Kaplan et al. (2020) findings that favoured pouring more compute into larger, lightly-trained models, reframing that generation of AI as undertrained. However, a 2024 replication study by Besiroglu and colleagues found inconsistencies in Chinchilla's published coefficients, suggesting the precise 20-token ratio is softer than widely assumed, though the core qualitative finding holds. The deeper reason labs like Meta have since moved beyond Chinchilla's rule is economic: a deployed model incurs inference costs of roughly 2N FLOPs per generated token for its entire lifetime, making a smaller, heavily overtrained model far cheaper to serve at scale. Training a 7B model on 15 trillion tokens — as with Llama 3 — costs more upfront than compute-optimal but yields significant long-term savings compared to serving a much larger model.

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