Post-Training Quantization
Definition
Post-training quantization reduces the numerical representation size of an already trained model.
Quantization of an already trained model without full quantization-aware retraining; depending on the method, calibration data or optimization steps are required.
Simply explained
Learned values are subsequently mapped to coarser numerical levels instead of retraining the entire model.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An FP32 model is mapped to INT8 using calibration data and tested against quality targets.
Limits and pitfalls
- Equating file size with speed or omitting representative calibration data.
Origin & history
PTQ became widespread with edge and inference optimization.
Use in mid-sized companies
- Possible application: An FP32 model is mapped to INT8 using calibration data and tested against quality targets.
Frequently asked questions
What does post-training quantization mean?
Quantization of an already trained model without full quantization-aware retraining; depending on the method, calibration data or optimization steps are required. What mistakes should be avoided? Equating file size with speed or omitting representative calibration data.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: