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 United Kingdom of Great Britain and Northern Ireland, in March 2021. It comes out of industry.
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.
How it was trained
Producing it required around 4.4 × 10²¹ FLOP of arithmetic, on Google TPU v3, which is a statement about the training budget rather than about inference.
It was trained on about 1,000,000,000,000 tokens of text.
Answers
DCTransformer (ImageNet) — common questions
How much compute was used to train DCTransformer (ImageNet)?
Around 4.4 × 10²¹ FLOP, on 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.
What GPU do I need to run DCTransformer (ImageNet)?
None. DCTransformer (ImageNet) 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 DCTransformer (ImageNet) open source?
No. DCTransformer (ImageNet) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DCTransformer (ImageNet) have?
DCTransformer (ImageNet) has 736M parameters. 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.
Who created DCTransformer (ImageNet)?
DCTransformer (ImageNet) was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was DCTransformer (ImageNet) released?
DCTransformer (ImageNet) 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.
What is DCTransformer (ImageNet) used for?
DCTransformer (ImageNet) works in Image generation, and is recorded as handling 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.