Parameters and weights

Plain English. The numbers a model learns during training — billions of them — that encode everything it knows. "Weights" and "parameters" are the same thing; a "70B model" has seventy billion of them. Shipping the weights is shipping the model.

Why it moves money. Weights are the asset. Training converts hundreds of millions of dollars of compute into a file, and everything about that file's economics follows from parameter count: how much memory it needs, what hardware can serve it, what it costs per token. Weights are also the theft target, the export-control object and the open-source battleground — "open weights" means the asset is free and the business must live elsewhere. But count is a size measure, not a quality measure: parameter-count comparisons across architectures and generations mislead more than they inform.

What to watch. Capability per parameter. The trend of small models doing work assumed to need frontier scale is the quiet deflationary force in AI — it moves inference from data centres to devices and undercuts every business plan priced on big-model serving.

From the signals. Kimi K3 became the first open model at 2.8 trillion parameters. A 150M-parameter model reasoned at $0.0007 per task. A 744-billion-parameter model now runs on 25GB of consumer RAM.