Quantization
Mixed-precision serving assigns different numeric precisions to tensors, operations, or layers in one model configuration. Sensitive paths retain wider formats while less sensitive paths use narrower ones.
Selective precision can recover quality while keeping much of the memory or compute benefit. Format conversions, fallback kernels, and poor placement can offset the intended gain.
Teams vary precision by component, then measure quality and serving behavior against a consistent baseline. The accepted map records every wider fallback so results remain reproducible.
Mixed-precision serving assigns different numeric precisions to tensors, operations, or layers in one model configuration. Sensitive paths retain wider formats while less sensitive paths use narrower ones. Teams vary precision by component, then measure quality and serving behavior against a consistent baseline. The accepted map records every wider fallback so results remain reproducible.
Selective precision can recover quality while keeping much of the memory or compute benefit. Format conversions, fallback kernels, and poor placement can offset the intended gain.
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