AI Glossary
What are parameters in an AI model?
Parameters: Parameters are the internal numbers a model adjusts during training — its "knowledge dials". More parameters generally means a more capable, more expensive model.
In plain English
Every fact, pattern and style a model absorbs is stored as slight adjustments across billions of numbers. No single parameter means anything — like no single neuron in your brain stores your grandmother — but together they encode everything the model "knows". An "8B model" has 8 billion of them; frontier models have far more.
Why it matters
- The rough size label for models (7B, 70B, …) and a proxy for capability
- More parameters = more memory and compute to run — small models exist for a reason
- Capability also depends heavily on data quality, not just count
A concrete example
A 3B-parameter model can run on a phone and handle summaries; coding agents leaning on subtle reasoning still go to far larger models.
Related terms
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