quant-recipe-search

AutoQuant exploration systematically designs iterative quantization experiments across formats and calibration strategies, establishing candidate tracking and promotion rules to select validated optimization recipes that preserve benchmark quality.

To structure PTQ calibration strategies, establish baselines, define comparison criteria, and set promotion rules for selecting validated optimization recipes that meet accuracy constraints and runtime compatibility checks.

Benchmark validation during quantization requires tracking structured candidates, checking runtime compatibility, and comparing optimized model performance against baseline model performance to ensure accuracy constraints are met.

Yes, you can use ModelOpt for deployment-aware quantization recipe selection by exploring candidates, validating benchmark quality, and checking runtime compatibility to optimize throughput and memory reduction.

You need iterative quantization searches when optimizing machine learning models for inference throughput or memory reduction, requiring structured candidate tracking and benchmark validation to preserve baseline quality.