What is dynamic artificial intelligence model quantization?
Definition
Dynamic artificial intelligence model quantization can preserve more precision for sensitive parts of a model while compressing other parts more aggressively. Some approaches determine scales or convert activations at runtime, while local-model formats may vary bit allocation across weight groups or layers.
The goal is to reduce memory further without causing the quality loss of a uniformly aggressive setting. Results depend on the exact method, calibration, model architecture and whether the inference software implements the format efficiently.
Acronyms and aliases
dynamic AI model quantization acronymdynamic quantization variant
General terms
Frequently asked questions
How is dynamic quantization different from uniform four-bit quantization?
Dynamic methods can vary precision or scaling across components or runtime values, while a uniform format applies a more consistent representation.
Why can dynamic quantization use less memory?
It can allocate very low precision where the model tolerates it and retain extra precision only for components that are more sensitive.
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