# What is Parameters? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Foundational AI  
> **Canonical Citation:** [https://spidits.com/ai-glossary/parameters](https://spidits.com/ai-glossary/parameters)

## Definition
Parameters are the internal configuration variables of an AI model that are learned automatically from training data. In a neural network, parameters consist of weights (which determine connection strength) and biases (which offset activation curves).

## Architectural Mechanics & Deep Dive
Parameters (weights and biases) are the internal configuration variables of a neural network that are adjusted during the training phase to minimize prediction error. The number of parameters (e.g., 8 billion, 70 billion) represents the model's capacity and scale; more parameters allow the model to learn more complex relationships, but increase compute and memory usage.

## Industry Applications & Real-World Use Cases
Model size description, VRAM requirement calculations, and model parameter scaling

## Frequently Asked Questions

### What does "7B model" mean?
It means the model has 7 billion parameters. Larger parameter counts generally indicate higher reasoning capacity but require more memory.

### How are weights updated?
During training, backpropagation calculates gradients, and optimization algorithms adjust the weights to minimize the loss function.

## Related Terminology
- [models](https://spidits.com/ai-glossary/models)
- [neural-network](https://spidits.com/ai-glossary/neural-network)
- [hyperparameter](https://spidits.com/ai-glossary/hyperparameter)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*