Meta's Generative Ads Model (GEM)
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
- 10 November 2025
- Authors
- Huayu Li, Xiaoyi Liu, Jade Nie, Ellie Wen, Chunzhi Yang, Jiyan Yang, Nancy Yu, Habiya Beg, Gil Arditi, Neeraj Bhatia
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Recommendation
- Task
- Recommender system
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.
- Training data
- tokens
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
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
- Record confidence
- Likely
From - https://www.facebook.com/business/news/ai-innovation-in-metas-ads-ranking-driving-advertiser-performance - GEM is in use, and Meta's platforms have many millions or billions of monthly active users.
Sources
Where this record came from and when it was last checked.
- Reference
- Meta’s Generative Ads Model (GEM): The Central Brain Accelerating Ads Recommendation AI Innovation
- Last updated
- 8 April 2026
What the numbers mean
Where it came from
Meta's Generative Ads Model (GEM) was published by Meta AI, in the country recorded as United States of America, during November 2025. It comes out of an organisation categorised as industry.
It works in the domain of Recommendation, and is recorded as performing the task of recommender system.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Its inclusion criterion: significant use.
Answers
Meta's Generative Ads Model (GEM) — common questions
Meta's Generative Ads Model (GEM)— who created it?
It was published by Meta AI, based in United States of America, an organisation categorised as industry.
Meta's Generative Ads Model (GEM)— when was it released?
It was published in November 2025.
Meta's Generative Ads Model (GEM)— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of recommender system. These are the areas it was designed around; they describe intent rather than a hard boundary.
Meta's Generative Ads Model (GEM)— 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.
Meta's Generative Ads Model (GEM)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Meta's Generative Ads Model (GEM)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
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.