Equation 23 · The Real Economics of Shipping a Model to a Device
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol Q^*_device
evice is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
subscript
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.
superscript
A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.
See an illustrated explanation →How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
The equation also explains something the variant-matrix section set up: rises with every additional tier, chip target and quantization scheme a release has to validate, which means rises too. A feature built once and validated across three device tiers has a lower bar to clear than the same feature validated across six, holding query volume constant. This is a real, and rarely stated, reason product teams should expect on-device placement to make sense for a small number of high-frequency, broadly used capabilities — the ones that clear by a wide margin — and to make less sense for a long tail of niche features that a smaller…
Read the full surrounding passage
The equation also explains something the variant-matrix section set up: rises with every additional tier, chip target and quantization scheme a release has to validate, which means rises too. A feature built once and validated across three device tiers has a lower bar to clear than the same feature validated across six, holding query volume constant. This is a real, and rarely stated, reason product teams should expect on-device placement to make sense for a small number of high-frequency, broadly used capabilities — the ones that clear by a wide margin — and to make less sense for a long tail of niche features that a smaller fraction of the fleet ever triggers, even when each individual inference is nominally free at the margin. The fleet still has to receive the build whether or not most of its members ever use it, and that is exactly what U , the update-distribution term, prices.
Sources cited in the article section
These citations give research context. Read each source to check which claims it supports.
Return to The Real Economics of Shipping a Model to a Device