Image generation
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 Amsterdam
- Organisation type
- Academia
- Country
- Netherlands
- Published
- 20 December 2013
- Authors
- DP Kingma, M Welling
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image clustering
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
- 784K
- Training data
- 47,040,000 tokens
"We trained generative models (decoders) and corresponding encoders (a.k.a. recognition models) having 500 hidden units in case of MNIST" 784*500*2=784000 (Ignoring latent dimension)
"We trained generative models of images from the MNIST and Frey Face datasets" MNIST has 60k images https://en.wikipedia.org/wiki/MNIST_database Frey Face has 2k images https://cs.nyu.edu/~roweis/data.html
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
- 4.8 × 10¹⁴ FLOP
- How it was established
- Third-party estimation
From https://openai.com/blog/ai-and-compute/ Appendix "less than 0.0000055 pfs-days" (86400*10^15*0.0000055) Figure 2 shows evaluations with 10^8 training samples 6*784000*100000000=470400000000000
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
- Confident
- Citations
- 21,760
Sources
Where this record came from and when it was last checked.
- Reference
- Auto-Encoding Variational Bayes
- Last updated
- 28 November 2025
What the numbers mean
Background
Image generation was published by University of Amsterdam, in the country recorded as Netherlands, during December 2013. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of image clustering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required arithmetic totalling around 4.8 × 10¹⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 47,040,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Image generation — common questions
Image generation— how many parameters does it have?
It has a parameter count of 784K. "We trained generative models (decoders) and corresponding encoders (a.k.a. recognition models) having 500 hidden units in case of MNIST" 784*500*2=784000 (Ignoring latent dimension). 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.
Image generation— who created it?
It was published by University of Amsterdam, based in Netherlands, an organisation categorised as academia.
Image generation— when was it released?
It was published in December 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Image generation— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image clustering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Image generation— how much compute was used to train it?
Training consumed around 4.8 × 10¹⁴ FLOP. 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.
Image generation— 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.
Image generation— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.