Gopher (7.1B)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 8 December 2021
- Authors
- Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 7.1B
- Training data
- 300,000,000 tokens
- Epochs
- 1
7.1B
"We train all models for 300 billion tokens with a 2048 token context window, using the Adam (Kingma and Ba, 2014) optimiser." 1 token ~ 0.75 words
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 1,605
- Benchmark data
- Gopher (7.1B)
Sources
Where this record came from and when it was last checked.
- Reference
- "Scaling Language Models: Methods, Analysis & Insights from Training Gopher"
- Last updated
- 25 May 2026
What the numbers mean
Background
Gopher (7.1B) was published by DeepMind, in United Kingdom of Great Britain and Northern Ireland, in December 2021. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Around 300,000,000 tokens went into training it.
Answers
Gopher (7.1B) — common questions
What is Gopher (7.1B) used for?
Gopher (7.1B) works in Language, and is recorded as handling language modeling/generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Gopher (7.1B)?
None. Gopher (7.1B) is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Gopher (7.1B) open source?
No. Gopher (7.1B) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gopher (7.1B) have?
Gopher (7.1B) has 7.1B parameters. 7.1B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Gopher (7.1B)?
Gopher (7.1B) was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Gopher (7.1B) released?
Gopher (7.1B) was published in December 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.