DLRM-12T
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,Carnegie Mellon University (CMU)
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 12 April 2021
- Authors
- Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Li…
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.
- Parameters
- 12T
- Training data
- tokens
They instantiated a 12T-parameter model to show that their hardware setup can train it despite the huge memory requirements.
No training details provided.
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 A100
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
- Citations
- 197
Sources
Where this record came from and when it was last checked.
- Reference
- Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
- Last updated
- 25 May 2026
What the numbers mean
Background
DLRM-12T was published by Meta AI,Carnegie Mellon University (CMU), in the country recorded as United States of America, during April 2021. The publishing organisation is categorised as industry,Academia.
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.
Answers
DLRM-12T — common questions
DLRM-12T— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DLRM-12T— how many parameters does it have?
It has a parameter count of 12T. They instantiated a 12T-parameter model to show that their hardware setup can train it despite the huge memory requirements. 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.
DLRM-12T— who created it?
It was published by Meta AI,Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as industry,Academia.
DLRM-12T— when was it released?
It was published in April 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.
DLRM-12T— 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.
DLRM-12T— 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.
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.