# Parameters

Concept · [Building with AI](https://ecosystem.samcarlton.com/ai)

Also called: parameter, params, model parameters, billion parameters, model size, parameter count

The values a model learns during training. [Weights](https://ecosystem.samcarlton.com/weights) are the main kind, so people often use the two words to mean the same thing. The count of parameters is a common way to describe the size of a model.

**For example:** One model has 8 billion parameters and another has 70 billion. The larger one needs more memory and a stronger [GPU](https://ecosystem.samcarlton.com/gpu) to run.

## Connects to (8)

- [Weights](https://ecosystem.samcarlton.com/weights): The numbers inside a model that it learned during training.
- [Model](https://ecosystem.samcarlton.com/model): The trained system at the center of an AI product.
- [Training](https://ecosystem.samcarlton.com/training): Teaching an AI model by showing it a very large number of examples before anyone uses it.
- [GPU](https://ecosystem.samcarlton.com/gpu): Graphics processing unit.
- [Neural network](https://ecosystem.samcarlton.com/neural-network): A kind of software model made of many layers of simple math units connected by weights, loosely inspired by the brain.
- [Latent space](https://ecosystem.samcarlton.com/latent-space): The internal map of numbers where a model places the things it reads.
- [Transformer](https://ecosystem.samcarlton.com/transformer): The design that most modern language models are built on, introduced by Google researchers in 2017.
- [Parametric knowledge](https://ecosystem.samcarlton.com/parametric-knowledge): What a model knows from its training, stored in its parameters.

Map view: https://ecosystem.samcarlton.com/parameters