Quantization: what it means.
Model quantization uses lower-precision representations for values such as weights or activations. NVIDIA describes workflows that can reduce memory requirements and support more efficient inference, with supported formats depending on the implementation. (NVIDIA: quantization)
Post-training quantization and quantization-aware training are different workflows. Calibration and evaluation data matter, and lower precision is not automatically acceptable for every layer or application. (NVIDIA: quantization)
Put the definition to work.
Do not compare two accelerator claims without checking their numerical formats. A lower-precision deployment should be tested with the actual model and hardware rather than assumed equivalent. (NVIDIA: quantization)
Questions to ask next.
Use these questions as a reading checklist, not as a device-selection recommendation. Exact manufacturer documentation takes priority over a general definition.
- Supported numeric formats
- Calibration and validation data
- Actual runtime compatibility and output quality
For the broader explanation, compare ai processing roles. This page defines the term; the companion guide connects it to the rest of the system.
Common Quantization questions.
Does a smaller model always produce the same outputs?
Do not assume equivalence. Quantization can affect accuracy, so evaluate the actual model and task after conversion and on the intended runtime. (NVIDIA: quantization)