The billions of numbers a model learns during training, and what a label like "7B" or "70B" is really telling you.
A model's parameters are the numbers inside it that get tuned during training. They are where everything the model "knows" is stored. A modern language model has billions of them, which is why you see names like Llama 7B or a 70B model.
The count is a rough measure of a model's capacity: how much pattern and nuance it can hold. It is the single number people reach for when sizing a model, though it is far from the whole story.
Think of each parameter as a tiny dial. Training turns billions of these dials, a little at a time, until the model predicts text well. The final settings, taken together, are the model's weights. There is no separate fact store: the knowledge is spread across all of them.
The B is billions of parameters. A 7B model has about seven billion dials; a 70B model has ten times more. Bigger models generally capture more nuance and handle harder tasks, but they also need much more memory and compute to run.
Beyond raw size, quality of training data, the training recipe, and fine-tuning matter enormously. A well-trained smaller model can beat a poorly trained larger one on real tasks. And a huge model is slower and costlier to serve, which is often the wrong tradeoff for everyday use.
Parameter count drives how much hardware a model needs and how fast it responds. Small models can run on a single GPU or even a laptop; the largest need clusters. This is why providers offer a range: a small fast model for routine work and a large one for the hardest problems.
Imagine a mixing board with billions of sliders. Training is the long process of setting every slider so the output sounds right. The parameter count is just how many sliders the board has. More sliders allow finer control, but only if they were set well.
Rather than defaulting to the biggest model for everything, Berges AI matches the model to the task, using a lean, fast one where that is enough and a larger one when the problem needs it. The model pages list the sizes so the tradeoff is transparent.
Try Berges AIUsually more capable, but not always smarter in practice. Training quality and tuning matter just as much, and a well-built smaller model can outperform a larger, weaker one.
They are nearly the same thing. Parameters are the tunable values in the model; weights are those values after training. People use the terms almost interchangeably.
Big models are slower and far more expensive to run. For most tasks a smaller model answers just as well, much faster and cheaper, which is why providers keep a range on offer.