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Equation 21 · The Real Economics of Shipping a Model to a Device

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CdevC_{\mathrm{dev}}

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the fixed, per-release engineering and QA cost of producing and validating the device-tier variant matrix described above — the harness time, the pass/fail sign-off per tier, per quantization scheme, per chip target. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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CdevC_{\mathrm{dev}}

Symbol C_dev

the fixed, per-release engineering and QA cost of producing and validating the device-tier variant matrix described above — the harness time, the pass/fail sign-off per tier, per quantization scheme, per chip target.

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subscript

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.

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How to interpret it

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What the article says around this equation

The equation also explains something the variant-matrix section set up: CdevC_{\mathrm{dev}} rises with every additional tier, chip target and quantization scheme a release has to validate, which means Qdevice∗Q^{*}_{\mathrm{device}} 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 Qdevice∗Q^{*}_{\mathrm{device}} by a wide margin — and to make less sense for a long tail of niche features that a smaller…
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The equation also explains something the variant-matrix section set up: CdevC_{\mathrm{dev}} rises with every additional tier, chip target and quantization scheme a release has to validate, which means Qdevice∗Q^{*}_{\mathrm{device}} 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 Qdevice∗Q^{*}_{\mathrm{device}} 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.

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Sources cited in the article section

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