An AI inference cloud is GPU infrastructure specifically optimized and sized for running trained models to serve predictions or generate outputs, as distinct from infrastructure built for training models from scratch.
Inference workloads generally need less raw compute per job than training, but often demand lower latency and more elastic scaling to handle unpredictable request volume.
Infrastructure purpose-built for inference can be right-sized for cost and responsiveness, instead of over-provisioning training-scale GPU capacity for workloads that don't need it.
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