Why are Large Language Models called “large”?

Large Language Models are called “large” because they are built and trained at a very large scale.

The word large mainly refers to three things:

• Many parameters: LLMs can contain millions, billions, or even more learned parameters. These parameters are the numerical values adjusted during training that help the model represent patterns in language.
• Large training datasets: They are trained on enormous collections of text and, in some cases, other data such as code.
• Large amounts of computation: Training an LLM typically requires substantial computing power to process the data and update its parameters.

Does “large” mean only parameter count?

Parameter count is one of the most common ways to describe an LLM's size, but it is not the only factor.

Two models with similar parameter counts can perform differently because of differences in:

• Training data quality
• Model architecture
• Training methods
• Amount of training computation
• Fine-tuning or post-training techniques

There is also no universal parameter threshold at which a language model officially becomes a Large Language Model.

For a detailed explanation of model parameters, see “What are parameters in an LLM?”

In short: Large Language Models are called “large” because they operate at large scale—especially in their number of parameters, training data, and computational requirements.