How Runpod Serverless places workers when GPUs are scarce

Runpod Serverless has updated its GPU placement scheduler to evaluate all compatible GPU types configured for an endpoint at once, rather than exhausting the highest-priority choice before considering fallbacks, helping workers avoid contention on popular GPUs such as H100s. Endpoints can list up to three GPU types in priority order, and the scheduler now balances that preference against each machine’s likelihood of successfully starting and retaining a worker; after the change, average machines considered per placement rose from about 38 to 47, more deployment requests considered multiple GPU types, and placements on second- or third-choice types increased by more than six percentage points. The reported results are associative before-and-after measurements rather than a controlled experiment, and worker-starvation effects may take longer to appear. Users are advised to include every GPU tier their models can reliably run on, size workloads to the lowest VRAM tier selected, allow broader data-center access where possible, and use at least five workers if they want proactive distribution across GPU choices in addition to reactive fallback. Broader GPU compatibility can improve availability during demand spikes, though fallback GPUs may deliver different performance and are billed at their own rates.

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