DALL·E 2
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
- 6 April 2022
- Authors
- Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
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
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
- 3.5B
- Training data
- 167,050,000,000 tokens
"Our decoder architecture is the 3.5 billion parameter GLIDE model"
"When training the encoder, we sample from the CLIP [39] and DALL-E [40] datasets (approximately 650M images in total) with equal probability"
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
- 3.4 × 10²³ FLOP
- How it was established
- Third-party estimation
Decoder architecture is similar to Imagen (1.46E+22), but trained on 1.6e9 datapoints (Table 3) rather than Imagen's 5.1e9 datapoints. DALL-E 2 uses two models as priors. I estimate the prior model's FLOP as 6*N*D = 6 * 1e9 * 4096 * 1e6 = 2.5e19 FLOP. However, this seems low compared to CLIP. So it may be possible to estimate DALL-E 2's compute by analogy to Imagen, but there is a lot of uncertainty and more research would be needed. here (https://arxiv.org/pdf/2407.15811) they claim the DALL…
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.
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Speculative
- Citations
- 8,200
Table 2
Sources
Where this record came from and when it was last checked.
- Reference
- Hierarchical Text-Conditional Image Generation with CLIP Latents
- Last updated
- 1 January 2026
What the numbers mean
What this model is
DALL·E 2 was published by OpenAI, in the country recorded as United States of America, during April 2022. The category the publisher falls under is industry.
It works in the domain of Image generation, and is recorded as performing the task of text-to-image, 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 3.4 × 10²³ FLOP. 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 167,050,000,000 tokens of text.
Its inclusion criterion: highly cited,SOTA improvement.
Answers
DALL·E 2 — common questions
DALL·E 2— how much compute was used to train it?
Training consumed around 3.4 × 10²³ FLOP. 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.
DALL·E 2— 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.
DALL·E 2— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DALL·E 2— how many parameters does it have?
It has a parameter count of 3.5B. "Our decoder architecture is the 3.5 billion parameter GLIDE model". 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.
DALL·E 2— who created it?
It was published by OpenAI, based in United States of America, an organisation categorised as industry.
DALL·E 2— when was it released?
It was published in April 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DALL·E 2— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of text-to-image, 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.