Parameter
Also: parameters
One of the billions of adjustable numbers inside a model. Training sets them; using the model does not change them.
A model is a huge set of dials, each holding a number. Training turns the dials a little at a time, over millions of examples, until the model’s guesses are good. After training the dials are locked: using the model does not change them, and showing it your document does not teach it anything permanent.
The fuller definition
A parameter is a numerical value learned during model training. Neural-network parameters include weights and biases used in computation.
Go deeper
Idealized value storage only, in decimal GB. Metadata, caches, mixed precision, and runtime overhead add to actual requirements. This is not a GPU purchasing calculator.
A concrete example
A model described as 8B has roughly eight billion parameters, subject to the publisher's counting convention.
The distinction that matters
Parameter count alone does not establish accuracy, speed, or the memory needed by the complete running system.
Related terms
Model weights
The full set of numbers that make up a trained model. Download the weights and you have the model.
Training & generationTraining
Adjusting a model with examples and a learning rule. The aim is to improve how it handles tasks.
Hardware & hostingQuantization
Storing model numbers with less detail. This can save memory. The effect on quality depends on the model and the task.
Where it is taught
What actually happens when you ask AI a question?Source and further reading
Google · Machine Learning GlossaryThe definition and example are written for this library. The link gives background, official documentation or legal authority as labelled. Legal scope depends on the jurisdiction and current text; product features depend on release and settings.