Glossary Library

Technical GlossaryGenerative AI and LLM

Quantization Aware Training

An approach that trains the model under low-precision conditions to preserve quality after quantization.

QAT aims to reduce later quality loss by exposing the model to quantization effects during training itself. Although more costly than PTQ, it can preserve accuracy better in some tasks. It is especially valuable in production settings with strict hardware constraints.

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Gradient Descent

A fundamental optimization method that updates parameters in the opposite direction of the gradient to minimize a loss function.

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Stochastic Gradient Descent

An optimization approach that updates parameters using single examples or small subsets instead of the full dataset at each step.

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Adam Optimization

A popular optimization algorithm that combines adaptive learning rates with momentum-like behavior.