VQ-VAE
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
- 2 November 2017
- Authors
- Aäron van den Oord, O. Vinyals, K. Kavukcuoglu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Representation learning
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
- 62,914,560,000 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
- Open source
Apache 2.0 https://github.com/google-deepmind/sonnet/blob/v1/sonnet/python/modules/nets/vqvae.py
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Neural Discrete Representation Learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
VQ-VAE was published by DeepMind, in United Kingdom of Great Britain and Northern Ireland, in November 2017. industry is the category the publisher falls under.
It works in Vision, and is recorded as doing representation learning.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
It was trained on about 62,914,560,000 tokens of text.
The reason it appears in this catalogue at all is highly cited.
Answers
VQ-VAE — common questions
Is VQ-VAE open source?
No. VQ-VAE has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does VQ-VAE have?
No parameter count has been published for VQ-VAE, which is why no memory or speed figure appears on this page.
Who created VQ-VAE?
VQ-VAE was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was VQ-VAE released?
VQ-VAE was published in November 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is VQ-VAE used for?
VQ-VAE works in Vision, and is recorded as handling representation learning. 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 VQ-VAE?
None. VQ-VAE 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.