Quantization Calibration Dataset

Data artifactModel ServingModelsarc:QuantizationCalibrationDataset

A sample of historical production-like inputs run through the model to observe per-layer activation ranges that set integer quantization parameters.

Responsibility. Supplies activation statistics for quantization parameter selection.

Also known as: Calibration set, Calibration data (calibration.npy)

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MUST be drawn from the production input distribution; calibrating on off-domain data (e.g., chit-chat for technical queries) enlarges quantization error.
SHOULD contain 500-1000 samples spanning common patterns (~80%), edge cases (~15%) and adversarial or challenging inputs (~5%).
MUST be drawn from the production workload domain (e.g., support transcripts for a support chatbot, code for a code model) rather than convenience corpora.
SHOULD contain 500-1000 examples spanning expected query types, domains and conversation styles.

As stated by the sources; verify before use.

Typically 500-1000 samples drawn from historical queries (Ch4.4).
Calibrating a legal document analyzer on generic web text degraded accuracy 15-20% on real legal queries (Ch4.6).
Beyond ~1000 examples, calibration time grows linearly while accuracy improves only logarithmically (Ch7.4).

Risks mitigated: Quantization error from unrepresentative calibration dataDomain mismatch between calibration and production dataActivation-distribution mismatch between calibration and production inputs

Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
Ch7.4: T. Nguyen, "TensorRT-LLM Fundamentals and Quantization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.4. ISBN: 9798244538229.
Ref7.01: NVIDIA, "Best practices," NVIDIA TensorRT Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html