DeepNet
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
- Microsoft Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 1 March 2022
- Authors
- Hongyu Wang, Shuming Ma, Li Dong, Shaohan Huang, Dongdong Zhang, Furu Wei
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation, Language modeling/generation
- Numerical format
- FP16
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
- 3.2B
- Training data
- 272,629,760,000 tokens
- Epochs
- 4
"Remarkably, on a multilingual benchmark with 7,482 translation directions, our 200-layer model with 3.2B parameters significantly outperforms the 48-layer state-of-the-art model with 12B parameters by 5 BLEU points, which indicates a promising scaling direction" EDIT 05/05/2022: The 12B model was presented in an earlier paper. This paper presents a 3.2B model
" The final data consists of 102 languages, 1932 directions, and 12B sentence pairs."
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.
- How it was established
- Comparison with other models
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
model code is said to be part of the torchscale library (I am not sure if full pre-training code is here): https://github.com/microsoft/torchscale MIT license
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
- 244
"Remarkably, on a multilingual benchmark with 7,482 translation directions, our 200-layer model with 3.2B parameters significantly outperforms the 48-layer state-of-the-art model with 12B parameters by 5 BLEU points"
Sources
Where this record came from and when it was last checked.
- Reference
- DeepNet: Scaling Transformers to 1,000 Layers
- Last updated
- 25 May 2026
What the numbers mean
What this model is
DeepNet was published by Microsoft Research, in United States of America, in March 2022. It comes out of industry.
It works in Language, and is recorded as doing language modeling, Translation, Language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It was trained on about 272,629,760,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
DeepNet — common questions
Who created DeepNet?
DeepNet was published by Microsoft Research, based in United States of America, categorised as industry.
When was DeepNet released?
DeepNet was published in March 2022. 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 DeepNet used for?
DeepNet works in Language, and is recorded as handling language modeling, Translation, Language modeling/generation. 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.
What GPU do I need to run DeepNet?
None. DeepNet 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.
Is DeepNet open source?
No. DeepNet has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DeepNet have?
DeepNet has 3.2B parameters. "Remarkably, on a multilingual benchmark with 7,482 translation directions, our 200-layer model with 3.2B parameters significantly outperforms the 48-layer state-of-the-art model with 12B parameters by 5 BLEU points, which indicates a promising scaling direction" EDIT 05/05/2022: The 12B model was presented in an earlier paper. This paper presents a 3.2B model. 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.
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.