Emu (Meta)
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 27 September 2023
- Authors
- Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yi Wen, Yiwen Song, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, Devi Parikh
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
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
- 2.8B
- Training data
- tokens
"We use a large U-Net with 2.8B trainable parameters."
"We curate a large internal pre-training dataset consisting of 1.1 billion images to train our model. The model is trained with progressively increasing resolutions"
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.
- Foundation model
- Yes
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Emu (Meta) was published by Meta AI, in United States of America, in September 2023. It comes out of industry.
It works in Image generation, and is recorded as doing text-to-image, Image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Emu (Meta) — common questions
Is Emu (Meta) open source?
No. Emu (Meta) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Emu (Meta) have?
Emu (Meta) has 2.8B parameters. "We use a large U-Net with 2.8B trainable parameters.". 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 Emu (Meta)?
Emu (Meta) was published by Meta AI, based in United States of America, categorised as industry.
When was Emu (Meta) released?
Emu (Meta) was published in September 2023. 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 Emu (Meta) used for?
Emu (Meta) works in Image generation, and is recorded as handling text-to-image, Image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Emu (Meta)?
None. Emu (Meta) 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.
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.