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Scaling Laws & Compute Training

Master the empirical power laws governing LLM performance, from Kaplan's original scaling results through Chinchilla-optimal ratios to modern inference-aware training strategies.

What you'll master

Kaplan scaling laws (parameters vs. data vs. compute)

Chinchilla-optimal training (Hoffmann et al.)

Compute-optimal model sizing

Inference-cost-aware scaling

Over-training and under-training regimes

Scaling law extrapolation for new architectures

Hard30 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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Code examplesArchitecture diagramsModel answersScoring rubricCommon pitfallsFollow-up Q&A

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