DCTransformer (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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 5 March 2021
- Authors
- Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia
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
- 736M
- Training data
- 1,000,000,000,000 tokens
parameters (Table 3): 738e6
[tokens] Table 3: Image resolution 384 batch size: 512 tokens processed: 1000e9 " We use a target chunk size of 896 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
- 4.4 × 10²¹ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 738 * 10^6 parameters * 1000 * 10^9 tokens = 4.428e+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
- Google TPU v3
- Chips used
- 128
- Power draw
- 116.8 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
- Unreleased
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 Images with Sparse Representations
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
DCTransformer (ImageNet) was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during March 2021. It comes out of an organisation categorised as industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required arithmetic totalling around 4.4 × 10²¹ FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 1,000,000,000,000 tokens of text.
Answers
DCTransformer (ImageNet) — common questions
DCTransformer (ImageNet)— how much compute was used to train it?
Training consumed around 4.4 × 10²¹ FLOP, on hardware recorded as Google TPU v3. 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.
DCTransformer (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.
DCTransformer (ImageNet)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DCTransformer (ImageNet)— how many parameters does it have?
It has a parameter count of 736M. parameters (Table 3): 738e6. 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.
DCTransformer (ImageNet)— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
DCTransformer (ImageNet)— when was it released?
It was published in March 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DCTransformer (ImageNet)— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.