---
title: "Parameter: what it means · The AI Compute Race · DRYBURGH"
source: https://dryburgh.com/vol1/glossary/parameter
description: "Parameter: One of the adjustable numbers inside a network that training sets, and the unit a model's size is counted in. The weights are their trained values."
---

# Parameter

What does “Parameter” mean?

ParameterOne of the adjustable numbers inside a network that training sets, and the unit a model’s size is counted in. The weights are their trained values. *Chapter 1.*

From *The AI Compute Race*. [Read the book](https://dryburgh.com/vol1#buy)

## Where the book uses it

- [Chapter 1 — The Stakes](https://dryburgh.com/vol1/notes/chapter-1)What the race is for: the belief that a lead in AI can compound into a lasting advantage, how long such leads have actually lasted, and what a trillion dollars a year is being bet against.

## Sources that use it

- Aakanksha Chowdhery et al., “PaLM: Scaling Language Modeling with Pathways,” arXiv:2204.02311, v1 submitted 5 April 2022, with Google Research’s announcement post of 4 April 2022 (the abstract: “a 540-billion parameter, densely activated, Transformer language model”, trained “on 6144 TPU v4 chips using Pathways”; the announcement post is titled “Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance”; v1’s reference list cites Hoffmann et al., “Training compute-optimal large language models”, arXiv:2203.15556, 2022; arXiv lists five versions, v1 of 5 April 2022 through v5 of 5 October 2022; Hoffmann appears 12 times in v1 and Chinchilla 17, against 29 in the version arXiv now serves, and the bibliography sets the title in sentence case, which a title-cased search misses). [In the Sources](https://dryburgh.com/vol1/sources/research#palm-chowdhery-2022)

## Related terms

- [Training](https://dryburgh.com/vol1/glossary/training)
- [Weights](https://dryburgh.com/vol1/glossary/weights)
- [Algorithmic efficiency](https://dryburgh.com/vol1/glossary/algorithmic-efficiency)
- [Application layer](https://dryburgh.com/vol1/glossary/application-layer)
- [Arsenal behind every arsenal](https://dryburgh.com/vol1/glossary/arsenal-behind-every-arsenal)
- [Capital expenditure](https://dryburgh.com/vol1/glossary/capital-expenditure)
- [Clocks of diffusion](https://dryburgh.com/vol1/glossary/clocks-of-diffusion)
- [Cluster](https://dryburgh.com/vol1/glossary/cluster)
- [Epoch AI](https://dryburgh.com/vol1/glossary/epoch-ai)
- [Exclusive generation](https://dryburgh.com/vol1/glossary/exclusive-generation)
- [Exclusivity of the model](https://dryburgh.com/vol1/glossary/exclusivity-of-the-model)
- [Falsifier](https://dryburgh.com/vol1/glossary/falsifier)

From the glossary of The AI Compute Race.

- [Campuses](https://dryburgh.com/vol1/ledger)
- [Tests](https://dryburgh.com/vol1/tests)
- [Scorecard](https://dryburgh.com/vol1/scorecard)
- [Data](https://dryburgh.com/vol1/data/releases)
- [Chapter by chapter](https://dryburgh.com/vol1/companion)
- [The record at a glance](https://dryburgh.com/vol1/observatory)
- [The machine](https://dryburgh.com/vol1/machine)
- [One campus, four futures](https://dryburgh.com/vol1/hyperion)
- [Glossary](https://dryburgh.com/vol1/glossary)
- [People](https://dryburgh.com/vol1/people)
- [Sources](https://dryburgh.com/vol1/sources)
- [Notes](https://dryburgh.com/vol1/notes)
- [Where the book was wrong](https://dryburgh.com/vol1/corrections)
- [Where its sources were wrong](https://dryburgh.com/corrections?book=vol1)
- [The Paper](https://dryburgh.com/vol1/paper)
- [Consent](https://dryburgh.com/vol1/consent)
- [Who builds and pays](https://dryburgh.com/vol1/parties)

*The AI Compute Race: Five Races, One Machine* is a book by Lee S. Dryburgh, the first volume of THE COMPUTE ECONOMY, published by Dryburgh, an imprint of Contestra OÜ, Tallinn, Estonia; its record closes on 8 November 2026.

[About the book](https://dryburgh.com/vol1)[Tell me when it is out](https://dryburgh.com/vol1#notify)