Variational Lossy Autoencoder (VLAE) MNIST
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
- University of California (UC) Berkeley,OpenAI
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 4 March 2017
- Authors
- Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image representation
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
- 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
Sources
Where this record came from and when it was last checked.
- Reference
- Variational Lossy Autoencoder
- Last updated
- 11 February 2026
What the numbers mean
About this model
Variational Lossy Autoencoder (VLAE) MNIST was published by University of California (UC) Berkeley,OpenAI, in the country recorded as United States of America, during March 2017. It comes out of an organisation categorised as academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of image representation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Variational Lossy Autoencoder (VLAE) MNIST — common questions
Variational Lossy Autoencoder (VLAE) MNIST— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image representation. 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.
Variational Lossy Autoencoder (VLAE) MNIST— 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.
Variational Lossy Autoencoder (VLAE) MNIST— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Variational Lossy Autoencoder (VLAE) MNIST— 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.
Variational Lossy Autoencoder (VLAE) MNIST— who created it?
It was published by University of California (UC) Berkeley,OpenAI, based in United States of America, an organisation categorised as academia,Industry.
Variational Lossy Autoencoder (VLAE) MNIST— when was it released?
It was published in March 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.
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.