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Published equation contexts

MTTFfleet≈1Nλ=MTTFdeviceN\mathrm{MTTF}_{\mathrm{fleet}} \approx \frac{1}{N\lambda} = \frac{\mathrm{MTTF}_{\mathrm{device}}}{N}

Why this formula appears here

Analysis. The following is my own structural analysis, not drawn from any single cited source. If a fleet of N identical devices each fails independently at rate λ\lambda , the fleet-wide failure rate is Nλ\lambda , so MTTFfleet≈1Nλ=MTTFdeviceN\mathrm{MTTF}_{\mathrm{fleet}} \approx \frac{1}{N\lambda} = \frac{\mathrm{MTTF}_{\mathrm{device}}}{N}. That relation is what licenses treating fleet-scale failure as a continuous background rate rather than a sequence of independent surprises, and it is the same move Meta’s reliability study makes in fitting a model to project Mean Time to Failure across GPU scales [ 3 ] . But it depends entirely on independence, and the sources above describe two ways that assumption breaks. First, correlated failure: a batch sharing a manufacturing defect or an upstream…

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NN

Symbol N

N occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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λ\lambda

Symbol λ

λ occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Its accuracy depends on the assumptions and range of use described in the article. Read it with the definitions, units, and assumptions supplied by the article.

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Published contexts (1)

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MTTFfleet≈1Nλ=MTTFdeviceN.\mathrm{MTTF}_{\mathrm{fleet}} \approx \frac{1}{N\lambda} = \frac{\mathrm{MTTF}_{\mathrm{device}}}{N}.

Equation 4 · Datacenters

AI Datacenter Systems Engineering in 2035: Scenarios, Signals, and Falsifiable Predictions

This equation gives an approximation: it relates the quantities while allowing an approximation.

Analysis. The following is my own structural analysis, not drawn from any single cited source. If a fleet of N identical devices each fails independently at rate λ\lambda , the fleet-wide failure rate is Nλ\lambda , so MTTFfleet≈1Nλ=MTTFdeviceN\mathrm{MTTF}_{\mathrm{fleet}} \approx \frac{1}{N\lambda} = \frac{\mathrm{MTTF}_{\mathrm{device}}}{N}. That relation is what licenses treating fleet-scale failure as a continuous background rate rather than a sequence of independent surprises, and it is the same move Meta’s reliability study makes in fitting a model to project Mean Time to Failure across GPU scales [ 3 ] . But it depends entirely on independence, and the sources above describe two ways that assumption breaks. First, correlated failure: a batch sharing a manufacturing defect or an upstream…

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