← All parts of this equation

Equation 18 · Part 2 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute

subscript

CtotalC_{\mathrm{total}}
subscript

What this part means

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

Its job in the formula

A subscript distinguishes a version, component, step, or member of a quantity. It does not automatically mean multiplication.

The passage around this formula

Hoffmann and colleagues then showed that the allocation between N and D at fixed C had been wrong in practice: for compute-optimal training, parameters and tokens should scale in roughly equal proportion, and models of the era were substantially undertrained relative to their size [ 6 ] . That result changed industry practice, but it optimises the wrong objective for a served product. Compute-optimal training minimises loss for a fixed training budget. A commercial system minimises total cost over training and inference, and when Q is very large the third term in CtotalC_{\mathrm{total}} dominates. The rational response is to train smaller models for longer than the compute-optimal recipe…

Read this part in the article →

Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

Open the illustrated subscripts: which member of a family? guide →

Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.