Griffin
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 29 February 2024
- Authors
- Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George Cristian-Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando De Freitas, Caglar Gulcehre
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat
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
- 14B
- Training data
- 300,000,000,000 tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.6 × 10²² FLOP
- How it was established
- Reported
Figure 1.a 10^(1/5)*10^22 = 15848931924611134852021.0137339150701326944213382503906831629
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v3
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
Sources
Where this record came from and when it was last checked.
- Reference
- Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Griffin was published by Google DeepMind, in United States of America, in February 2024. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Chat.
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
Training it took roughly 1.6 × 10²² FLOP of computation, on Google TPU v3 — a measure of what producing the model cost, not of how fast it answers.
Around 300,000,000,000 tokens went into training it.
Answers
Griffin — common questions
What is Griffin used for?
Griffin works in Language, and is recorded as handling language modeling/generation, Chat. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Griffin?
Around 1.6 × 10²² FLOP, on Google TPU v3. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Griffin?
None. Griffin 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 Griffin open source?
No. Griffin has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Griffin have?
Griffin has 14B parameters. 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 Griffin?
Griffin was published by Google DeepMind, based in United States of America, categorised as industry.
When was Griffin released?
Griffin was published in February 2024. 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.