Beyond Chinchilla-Optimal: Accounting for Inference in Language Model
  Scaling Laws
Nikhil Sardana Jonathan Frankle
Large language model (LLM) scaling laws are empirical formulas that estimate
changes in model quality as a result of increasing parameter count and training
data. However, these formulas, including the popular DeepMind Chinchilla
scaling laws, neglect to include the cost of inference. We modify the
Chinchilla scaling laws to calculate the optimal LLM parameter count and
pre-training data size to train and deploy a model of a given quality and
inference demand. We conduct our analysis both in terms of a compute budget and
real-world costs and find that LLM researchers expecting reasonably large
inference demand (~1B requests) should train models smaller and longer than
Chinchilla-optimal.