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The billions of numbers a model learns during training that encode everything it knows.
Parameters are the weights inside a neural network — each one is a number that gets tuned during training to minimize prediction error. When you hear '70B' or '405B', that's the parameter count, and it roughly tracks how much the model can know and how expensive it is to run. More params generally means smarter but slower and pricier; a 7B model runs on a laptop, a 400B model needs a cluster.
Plainly
Think of Parameters as the brain part that guesses or decides. The billions of numbers a model learns during training that encode everything it knows.
In practice
Use it when model choice, prompts, latency, cost, or quality affect the product result. In practice, define the owner, input, output, and failure mode before you rely on it.