GANs
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 Montreal / Université de Montréal
- Organisation type
- Academia
- Country
- Canada
- Published
- 10 June 2014
- Authors
- Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
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
- 120,000 tokens
"We trained adversarial nets an a range of datasets including MNIST[23], the Toronto Face Database (TFD) [28], and CIFAR-10 [21]." MNIST has 60k images https://en.wikipedia.org/wiki/MNIST_database TFD seems to have 2925 examples (?) https://www.cs.toronto.edu/~urtasun/courses/CSC411/hw3-411.pdf CIFAR-10 has 60k images https://www.cs.toronto.edu/~kriz/cifar.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
- 5.2 × 10¹⁷ FLOP
- How it was established
- Third-party estimation
From https://openai.com/blog/ai-and-compute/ Appendix "Less than 0.006 pfs-days" (86400*10^15*0.006) Seems extremely speculative, unless someone at OpenAI privately corresponded with the authors. There is no information about compute or training in the GANs paper.
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
- Speculative
- Citations
- 36,870
Sources
Where this record came from and when it was last checked.
- Reference
- Generative Adversarial Networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
GANs was published by University of Montreal / Université de Montréal, in Canada, in June 2014. It comes out of academia.
It works in Image generation, and is recorded as doing image generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 5.2 × 10¹⁷ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
It was trained on about 120,000 tokens of text.
The reason it appears in this catalogue at all is highly cited.
Answers
GANs — common questions
Is GANs open source?
The licensing for GANs was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does GANs have?
No parameter count has been published for GANs, which is why no memory or speed figure appears on this page.
Who created GANs?
GANs was published by University of Montreal / Université de Montréal, based in Canada, categorised as academia.
When was GANs released?
GANs was published in June 2014. 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 GANs used for?
GANs works in Image generation, and is recorded as handling image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train GANs?
Around 5.2 × 10¹⁷ 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.
What GPU do I need to run GANs?
None. GANs 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.