Model Quantization
Model quantization stores weights or activations with less exact number formats. This can lower memory use, data movement, and computing cost.
Another example
An engineer changes an FP16 model to INT8 and checks it with calibration samples and accuracy tests. It must meet memory and delay limits without losing more quality than allowed. Simply removing parameters is not quantization.
Main parts
- 01source precision
- 02scale and zero point
- 03quality calibration
Use it when
Use this term when lower number precision is the method used to make a model smaller or faster.
Do not use it when
Do not promise a speed gain until you test the target hardware and check how much quality changed.
Name used in code
FP16 weights → INT8 scales + values Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Model Quantization to make AI behavior measurable and tied to evidence. User context: An engineer changes an FP16 model to INT8 and checks it with calibration samples and accuracy tests. It must meet memory and delay limits without losing more quality than allowed. Simply removing parameters is not quantization. AI method or concept: Model Quantization. Why it fits: Use this term when lower number precision is the method used to make a model smaller or faster. Do not use it when: Do not promise a speed gain until you test the target hardware and check how much quality changed. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Model Quantization. Definition: Model quantization stores weights or activations with less exact number formats. This can lower memory use, data movement, and computing cost. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.