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Series
How local hardware learned to run giant AI — and why size stopped measuring intelligence.
A five-part series on why parameter count stopped measuring intelligence, and how ordinary machines started running extraordinary models.
| # | Essay | Status |
|---|---|---|
| 0 | Plain English Glossary | ✅ Published |
| 1 | Bigger Isn't Always Smarter | ✅ Published |
| 2 | How a Laptop Learned to Run a Giant | ✅ Published |
| 3 | The New Shape of AI Models | 🚧 Coming soon |
| 4 | Who Actually Saves Money? | 🚧 Coming soon |
| 5 | The Future Is Both | 🚧 Coming soon |
The Basics Term What it actually means Parameter A dial the model tuned while learning. Billions of them. Think of them as the model's settings. Model size (e.g., 70B) How many dials the model
New to the jargon? Start with the plain-English glossary. For years, the rule was simple: bigger AI meant smarter AI. More parameters, more brainpower. Every new model was just the last one, scaled u
New to the jargon? Start with the glossary → A 70-billion-parameter model is roughly 140 GB of data. A mid-range gaming laptop has between 8 and 16 GB of video memory. That's like trying to pour a sw
