Genie
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
- 23 February 2024
- Authors
- Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video, Games
- Task
- Video generation, Image-to-video, Text-to-video
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
- 10.7B
- Training data
- 942,000,000,000 tokens
- Batch size
- 512
When combined with the tokenizer and action model this brings the total to 10.7B parameters, trained on 942B tokens, which we refer to as the Genie model.
When combined with the tokenizer and action model this brings the total to 10.7B parameters, trained on 942B tokens, which we refer to as the Genie model.
As a result, for our final model, we train a 10.1B dynamics model with a batch size of 512, for a total of 125k steps, using 256 TPUv5p
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
- 6.6 × 10²² FLOP
- How it was established
- Reported
Table 12
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 v5p
- Chips used
- 256
- Power draw
- 273.6 kW
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
We have chosen not to release the trained model checkpoints, the model’s training dataset, or examples from that data to accompany this paper or the website. We would like to have the opportunity to further engage with the research (and video game) community and to ensure that any future such releases are respectful, safe and responsible.
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
- Genie: Generative Interactive Environments
- Last updated
- 28 November 2025
What the numbers mean
Background
Genie was published by Google DeepMind, in the country recorded as United States of America, during February 2024. It comes out of an organisation categorised as industry.
It works in the domain of Video, Games, and is recorded as performing the task of video generation, Image-to-video, Text-to-video.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 6.6 × 10²² FLOP, on hardware recorded as Google TPU v5p. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 942,000,000,000 tokens of text.
Answers
Genie — common questions
Genie— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Genie— how many parameters does it have?
It has a parameter count of 10.7B. When combined with the tokenizer and action model this brings the total to 10.7B parameters, trained on 942B tokens, which we refer to as the Genie model. 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.
Genie— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
Genie— when was it released?
It 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.
Genie— what is it used for?
It works in the domain of Video, Games, and is recorded as handling the task of video generation, Image-to-video, Text-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.
Genie— how much compute was used to train it?
Training consumed around 6.6 × 10²² FLOP, on hardware recorded as Google TPU v5p. 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.
Genie— what GPU do I need to run it?
None. This 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.
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.