MuZero VP9
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
- 14 February 2022
- Authors
- Amol Mandhane, Anton Zhernov, Maribeth Rauh, Chenjie Gu, Miaosen Wang, Flora Xue, Wendy Shang, Derek Pang, Rene Claus, Ching-Han Chiang, Cheng Chen, Jingning Han, Angie Chen, Daniel J. Mankowitz, Jackson Broshear, Julian Schrittwieser, Thomas Hubert, Oriol Vinyals, Timothy Mann
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Video compression
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.
- Training data
- 2,400,000,000 tokens
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
- Unknown
- Citations
- 48
Sources
Where this record came from and when it was last checked.
- Reference
- MuZero with Self-competition for Rate Control in VP9 Video Compression
- Last updated
- 25 May 2026
What the numbers mean
What this model is
MuZero VP9 was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during February 2022. It comes out of an organisation categorised as industry.
It works in the domain of Video, and is recorded as performing the task of video compression.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 2,400,000,000 tokens of text.
Answers
MuZero VP9 — common questions
MuZero VP9— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
MuZero VP9— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
MuZero VP9— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
MuZero VP9— when was it released?
It was published in February 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
MuZero VP9— what is it used for?
It works in the domain of Video, and is recorded as handling the task of video compression. 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.
MuZero VP9— 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.