Sparse Transformer (ImageNet)
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
- 152M
- Training data
- tokens
- Epochs
- 70
152M
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
- 1.5 × 10²¹ FLOP
- How it was established
- Hardware
125000000000000 FLOP / GPU /sec [V100 reported, bf16 assumed] * 64 GPUs * 168 hours * 3600 sec / hour * 0.3 [assumed utilization] = 1.45152e+21 FLOP
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
- 64
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 39.5 kW
"7 days on 64 V100 GPUs"
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
What this model is
Sparse Transformer (ImageNet) was published by OpenAI, in the country recorded as United States of America, during April 2019. The category the publisher falls under is 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.
How it was trained
Producing it required arithmetic totalling around 1.5 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Sparse Transformer (ImageNet) — common questions
Sparse Transformer (ImageNet)— how many parameters does it have?
It has a parameter count of 152M. 152M. 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.
Sparse Transformer (ImageNet)— who created it?
It was published by OpenAI, based in United States of America, an organisation categorised as industry.
Sparse Transformer (ImageNet)— when was it released?
It 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.
Sparse Transformer (ImageNet)— 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.
Sparse Transformer (ImageNet)— how much compute was used to train it?
Training consumed around 1.5 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. 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.
Sparse Transformer (ImageNet)— 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.
Sparse Transformer (ImageNet)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.