What are billions of parameters?
What a parameter is, how big a billion is, and why big models need racks of GPUs.
Transcript
A parameter is one adjustable number, a knob the model can turn.
A large model has billions of them, all set by training.
A million seconds is about twelve days. A billion seconds is about thirty two years.
Seventy billion seconds is over two thousand years. That is how many knobs there are to set.
Each parameter takes two bytes, so seventy billion is one hundred forty gigabytes.
Too big for one laptop, so large models run on racks of specialised G P Us.
More parameters means more room for patterns, but good data and training matter too. Billions of knobs, tuned by learning.
More in this series
1:14How does AI work?
Neurons and weights, learning from mistakes, and predicting the next word.
1:13How does AI read a sentence?
Tokens, embeddings and attention, stacked in layers to score the next token.
1:26How do networks learn from errors?
Loss as a landscape, gradient descent and backpropagation.
1:24How is a chatbot trained?
Pretraining, fine-tuning and human feedback turn a text predictor into an assistant.
1:23What does temperature do?
Scores become probabilities, and temperature sharpens or flattens them.
1:19How does AI draw pictures?
Diffusion models add noise to learn, then remove it to create, steered by a prompt.
1:15How can AI use your own documents?
Retrieval-augmented generation: embed, retrieve, augment the prompt, generate.
1:14Why does AI make things up?
Likely is not the same as true: gaps, snowballing errors, and what helps.
1:16How much can an AI remember?
The context window, forgetting, the cost of long inputs, and workarounds.
1:09What is an AI agent?
A model in a loop with tools and guardrails.
1:12Can AI be biased?
Skewed data, where bias comes from, proxies, and how to audit and fix it.
1:10What is overfitting?
Underfit, good fit and overfit curves, train vs test error, and the fixes.