Parameters

The number of parameters is often cited as a measure of model size, such as “7 billion parameters”. More parameters mean more capacity, but also more memory and higher computing costs.

Bigger is not automatically better: the quality and quantity of training data, the architecture and post-training matter just as much. Small, well-trained models sometimes beat much larger ones.