Write the policy for when a GPU is replaced rather than returned to service. What are the triggers and what do they cost?

Every replacement costs a spare, a maintenance window and a return process; every device kept costs the risk of a job it will fail. Four triggers that decide it without a case-by-case argument, the arithmetic that sets the repeat threshold, and why the device's history beats any diagnostic result.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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🩺 Fleet Reliability & Observability🔒 Premium

ECC, Row Remapping and Memory ErrorsHBM stacks flip bits, and the difference between a fleet that shrugs and one that loses a training step to corruption is error-correcting codes plus the machinery that retires bad memory before it produces a double-bit error. A single-bit error is corrected silently and counted; a double-bit error is detected, kills the process, and on Ampere and later triggers the row remapper to swap the failing row for a spare at the next reset. This page explains the codes, the remapper's states, how to read the counters as a prediction of failure, and the RMA rules a fleet applies.

Foundational

🩺 Fleet Reliability & Observability

GPU Failure Modes and XID ErrorsWhen a GPU misbehaves, the NVIDIA driver writes an XID line to the kernel log, and the number on that line is the first and often the only clue to what happened. Fleet engineers learn a dozen of them the way doctors learn a dozen lab values: 13 and 31 are almost always the application, 48 and 95 are memory that needs a reset, 63 and 64 are the row remapper reporting or failing, 74 is the NVLink fabric, 79 is a GPU that has vanished from the PCIe bus. This page gives the taxonomy, the decision for each (retry, reset, drain, RMA), and the derivation of how often a big fleet should expect each.

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🧮 Open Weights & Serving Engines

Capacity Planning for Open-Weights FleetsPlanning a fleet for a sparse open-weights model works differently from planning one for a dense model, because memory follows total parameters and throughput follows active parameters, and those now differ by more than twenty times. The sizing goes in one direction only: from a traffic forecast to tokens per second, to replicas at a measured operating point, to GPUs, to racks and kilowatts. Doing it in the other direction, from an available GPU count, produces a fleet that fits the hardware rather than the demand.

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🧭 Ownership & Judgment

Talking About Cost and Capacity with LeadershipInfrastructure engineers are asked to justify large numbers to people who do not share their vocabulary, and the conversations go wrong in predictable ways: a technical objection with no alternative, a forecast with no assumptions, or a cost quoted in a unit the listener cannot act on. What works is a small number of costed options, a stated recommendation, the decision needed by a date, and every figure expressed in whatever the listener actually controls.

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UP NEXT ON YOUR JOURNEY

Next in this trackHow much spare capacity does a 16,000-GPU fleet need, and what are you actually reserving it for?Next in this trackHow would you build a postmortem practice that people take seriously and that actually reduces incidents?Next in this trackYour fleet is replacing one percent of its GPUs every month. Is that normal, and how would you find out?

FEDITOR'S NOTE

Scored on written triggers rather than case-by-case judgment, on the cost of both errors, and on device history outweighing diagnostic results for intermittent faults.

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