DLRM-2020
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
- Facebook AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 31 May 2019
- Authors
- Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Recommendation
- Task
- Recommender system
- Numerical format
- FP32
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
- 100B
- Training data
- 38,571,428 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
- 4 × 10¹⁸ 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
- Open source
MIT, training/inference code: https://github.com/facebookresearch/dlrm
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 908
"In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM)"
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning Recommendation Model for Personalization and Recommendation Systems
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DLRM-2020 was published by Facebook AI, in the country recorded as United States of America, during May 2019. The publishing organisation is categorised as industry.
It works in the domain of Recommendation, and is recorded as performing the task of recommender system.
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 4 × 10¹⁸ 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 38,571,428 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
DLRM-2020 — common questions
DLRM-2020— how many parameters does it have?
It has a parameter count of 100B. 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-2020— who created it?
It was published by Facebook AI, based in United States of America, an organisation categorised as industry.
DLRM-2020— when was it released?
It was published in May 2019. 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-2020— what is it used for?
It works in the domain of Recommendation, and is recorded as handling the task of recommender system. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
DLRM-2020— how much compute was used to train it?
Training consumed around 4 × 10¹⁸ 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.
DLRM-2020— 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-2020— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.