What does this equation mean?

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 $C_{\mathrm{total}}$ dominates. The rational response is to train smaller models for longer than the compute-optimal recipe suggests, because every parameter removed is paid back on every one of Q requests. The existence of small, cheap variants in each OpenAI generation is consistent with exactly this trade, though the specific training recipes are not disclosed.