SAGAN
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
- Rutgers University,Google Research
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 14 June 2019
- Authors
- Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena
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
- tokens
- Epochs
- 8
" where k = 1, 2, 4, 8 after few training epochs on ImageNet. For memory efficiency, we choose k = 8 (i.e., C¯ = C/8) in all our experiments."
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
- 2.2 × 10²⁰ FLOP
- How it was established
- Hardware
Assuming (!) they used V100s: 125000000000000 FLOP / GPU / sec * 336 hours [see training time notes] * 3600 sec / hour * 4 GPUs * 0.3 [assumed utilization] = 1.8144e+20 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 4
- Wall-clock time
- 336 hours (14 days)
"Models were trained for roughly 2 weeks on 4 GPUs each"
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/brain-research/self-attention-gan
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Self-Attention Generative Adversarial Networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
SAGAN was published by Rutgers University,Google Research, in the country recorded as United States of America, during June 2019. The publishing organisation is categorised as academia,Industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 2.2 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
SAGAN — common questions
SAGAN— who created it?
It was published by Rutgers University,Google Research, based in United States of America, an organisation categorised as academia,Industry.
SAGAN— when was it released?
It was published in June 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SAGAN— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation. 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.
SAGAN— how much compute was used to train it?
Training consumed around 2.2 × 10²⁰ 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.
SAGAN— 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.
SAGAN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
SAGAN— 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.
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.