DLRM-2021
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
- 1 July 2020
- 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, …
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
- 1T
- Training data
- tokens
Figure 1 https://arxiv.org/abs/2104.05158
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 × 10²⁰ FLOP
- How it was established
- Reported
Figure 1 https://arxiv.org/abs/2104.05158
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
- High-performance, Distributed Training of Large scale Deep Learning Recommendation Models
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DLRM-2021 was published by Meta AI, in the country recorded as United States of America, during July 2020. 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.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 3 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
DLRM-2021 — common questions
DLRM-2021— 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.
DLRM-2021— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DLRM-2021— how many parameters does it have?
It has a parameter count of 1T. Figure 1 https://arxiv.org/abs/2104.05158. 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-2021— who created it?
It was published by Meta AI, based in United States of America, an organisation categorised as industry.
DLRM-2021— when was it released?
It was published in July 2020. 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-2021— 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-2021— how much compute was used to train it?
Training consumed around 3 × 10²⁰ 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.
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.