Glossary category
Quantization glossary
Definitions for inference serving terms in the quantization category.
Terms in this category
- FP8 quantizationFP8 quantization represents selected weights, activations, or cache values with eight-bit floating point formats.
- Channelwise quantizationChannelwise quantization assigns a separate scale to each channel instead of sharing one scale across an entire tensor.
- Quantization quality recoveryQuantization quality recovery is the measured retention of task performance after a model moves to lower precision.
- KV cache quantizationKV cache quantization stores cached attention keys and values in narrower numeric formats.
- AWQActivation-aware weight quantization uses activation statistics to identify weight channels that need extra protection.
- GPTQGPTQ is a layerwise post-training weight quantization method that uses approximate second-order information.
- Four-bit weight-only quantizationFour-bit weight-only quantization stores model weights as four-bit integers while keeping activations at higher precision.
- Eight-bit integer quantizationEight-bit integer quantization represents selected tensors with integer values and scale factors.
- Weight-only versus weight-activation quantizationWeight-only quantization narrows stored weights while activations remain at higher precision.
- FP8 versus eight-bit integer quantizationFP8 uses an exponent and mantissa within an eight-bit floating-point representation.
- Calibration dataCalibration data is a sample of model inputs used to derive quantization scales or protection choices.
- Outlier channelsOutlier channels contain values whose magnitudes are much larger than most peer channels.
- Mixed-precision servingMixed-precision serving assigns different numeric precisions to tensors, operations, or layers in one model configuration.
Related glossary categories
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