Artificial intelligence model parameters are numerical values adjusted during training. They include weights and biases that determine how strongly signals influence one another as data moves through a neural network. The collection of learned parameters encodes the patterns the model acquired from its training data.
Parameter count is often used as a rough measure of model capacity, but it does not by itself determine quality, speed, or memory use. Architecture, training data, numerical precision, and how many parameters are active for each token all matter. Mixture-of-experts systems are a clear example because their total parameter count can be much larger than the number used for one inference step.
Acronyms and aliases
model parameter variantparameter variant
Related terms
Frequently asked questions
What do model parameters store?
They store learned numerical relationships that shape how a model responds to input. They do not usually store source documents as directly readable records.
Does a larger parameter count always mean a better model?
No. More parameters can add capacity, but model architecture, training quality, data, evaluation results, and deployment constraints are also decisive.
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