Sparse Transformer (CIFAR10)
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
- OpenAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 23 April 2019
- Authors
- Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
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.
- Parameters
- 59M
- Training data
- tokens
- Epochs
- 120
59M
"We use 48000 examples for training and 2000 examples for validation"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
- Chips used
- 8
- Power draw
- 4.9 kW
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 (non-commercial)
some primitives are released here, but not the entire training code: https://github.com/openai/sparse_attention no clear license "Normally, implementing sparse attention would involve slicing query and key matrices in blocks, so to ease experimentation we implemented a set of block-sparse kernels which efficiently perform these operations on the the GPU. We open-source these kernels and provide example sparse attention functions in this repository(opens in a new window)."
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Generating Long Sequences with Sparse Transformers
- Last updated
- 11 February 2026
What the numbers mean
About this model
Sparse Transformer (CIFAR10) was published by OpenAI, in United States of America, in April 2019. It comes out of industry.
It works in Image generation, and is recorded as doing image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Sparse Transformer (CIFAR10) — common questions
Who created Sparse Transformer (CIFAR10)?
Sparse Transformer (CIFAR10) was published by OpenAI, based in United States of America, categorised as industry.
When was Sparse Transformer (CIFAR10) released?
Sparse Transformer (CIFAR10) was published in April 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.
What is Sparse Transformer (CIFAR10) used for?
Sparse Transformer (CIFAR10) 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.
What GPU do I need to run Sparse Transformer (CIFAR10)?
None. Sparse Transformer (CIFAR10) 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.
Is Sparse Transformer (CIFAR10) open source?
No. Sparse Transformer (CIFAR10) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Sparse Transformer (CIFAR10) have?
Sparse Transformer (CIFAR10) has 59M parameters. 59M. 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.
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.