DALL-E
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
- 5 January 2021
- Authors
- Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Text-to-image, Image generation
- Approach
- Self-supervised learning
- Numerical format
- FP16
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
- 12B
- Training data
- 320,000,000,000 tokens
- Epochs
- 1.76
DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions
"To scale up to 12-billion parameters, we created a dataset of a similar scale to JFT-300M (Sun et al., 2017) by collecting 250 million text-images pairs from the internet. " number of epochs: 1024 batch size * 430,000 updates / 250,000,000 = 1.76
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
- 4.7 × 10²² FLOP
- How it was established
- Third-party estimation
source: https://lair.lighton.ai/akronomicon/ archived: https://github.com/lightonai/akronomicon/tree/main/akrodb VAE training "on 64 16 GB NVIDIA V100 GPUs, with a per-GPU batch size of 8, resulting in a total batch size of 512. It is trained for a total of 3,000,000 updates." Transformer training: "We trained the model using 1024, 16 GB NVIDIA V100 GPUs and a total batch size of 1024, for a total of 430,000 updates."; "We concatenate up to 256 BPE-encoded text tokens with the 32 × 32 = 1024 …
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 Tesla V100 DGXS 16 GB
- Chips used
- 1,024
- Power draw
- 519.7 kW
- Compute cost
- $125,819
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
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Significant use,Highly cited,Historical significance
- Record confidence
- Likely
- Citations
- 6,403
Sources
Where this record came from and when it was last checked.
- Reference
- Zero-Shot Text-to-Image Generation
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DALL-E was published by OpenAI, in United States of America, in January 2021. The organisation is categorised as industry.
It works in Image generation, and is recorded as doing text-to-image, Image generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required around 4.7 × 10²² FLOP of arithmetic, on NVIDIA Tesla V100 DGXS 16 GB, which is a statement about the training budget rather than about inference.
The training set ran to roughly 320,000,000,000 tokens.
Its inclusion criterion is significant use,Highly cited,Historical significance.
Answers
DALL-E — common questions
When was DALL-E released?
DALL-E was published in January 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 DALL-E used for?
DALL-E works in Image generation, and is recorded as handling text-to-image, 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.
How much compute was used to train DALL-E?
Around 4.7 × 10²² FLOP, on NVIDIA Tesla V100 DGXS 16 GB. 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 DALL-E?
None. DALL-E 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 DALL-E open source?
No. DALL-E has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DALL-E have?
DALL-E has 12B parameters. DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions. 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 DALL-E?
DALL-E was published by OpenAI, based in United States of America, categorised as industry.
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.