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Inference & ServingHard· Scaling Laws· Cost Optimization

How does the IsoFLOP result change if you account for i...

The standard Chinchilla result minimizes training loss per FLOP, ignoring the lifecycle cost of the model. If a model is expected to serve R inference requests, the total cost becomes 6ND + 2NR. When fixing the total tokens processed, the cost-minimizing parameter count N is smaller than the compute-optimal N. Consequently, production models are often trained on significantly higher token-to-parameter ratios (100-2,000+ tokens per parameter) to optimize for total cost rather than just training efficiency.

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