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The engineer who cut a model's inference bill by 60 percent

A worked example of what we write: about a person, built on figures somebody else could check, with the claim and its evidence in the same paragraph.

Most teams respond to a rising inference bill by buying more hardware. When the cost of serving a mid-sized language model at a 40-person company doubled in a quarter, its lead engineer did the opposite and went looking for the waste.

The answer was in scheduling. Requests were being batched on arrival rather than by shape, so short prompts waited behind long ones and the accelerator sat idle between them. Rewriting the batching layer to group by sequence length lifted utilisation from 38 percent to 71 percent, measured over a fortnight of production traffic.

“The hardware was never the constraint,” they said. “We were just feeding it badly.”

The change cut monthly serving costs by roughly 60 percent on the same traffic. The approach has since been published as an internal engineering note and adopted by two other teams at the company.

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