Tensorized Transformer (large PTB)
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
- Tianjin University,Microsoft Research Asia,Beijing Institute of Technology
- Organisation type
- Academia,Industry,Academia
- Country
- China
- Published
- 24 June 2019
- Authors
- Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation
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
- Epochs
- 30
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 P40
- Chips used
- 2
- Power draw
- 1.0 kW
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
- 194
- Benchmark data
- Tensorized Transformer (large PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- A Tensorized Transformer for Language Modeling
- Last updated
- 25 May 2026
What the numbers mean
About this model
Tensorized Transformer (large PTB) was published by Tianjin University,Microsoft Research Asia,Beijing Institute of Technology, in China, in June 2019. academia,Industry,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Tensorized Transformer (large PTB) — common questions
What GPU do I need to run Tensorized Transformer (large PTB)?
None. Tensorized Transformer (large PTB) 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 Tensorized Transformer (large PTB) open source?
No. Tensorized Transformer (large PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Tensorized Transformer (large PTB) have?
No parameter count has been published for Tensorized Transformer (large PTB), which is why no memory or speed figure appears on this page.
Who created Tensorized Transformer (large PTB)?
Tensorized Transformer (large PTB) was published by Tianjin University,Microsoft Research Asia,Beijing Institute of Technology, based in China, categorised as academia,Industry,Academia.
When was Tensorized Transformer (large PTB) released?
Tensorized Transformer (large PTB) was published in June 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.
What is Tensorized Transformer (large PTB) used for?
Tensorized Transformer (large PTB) works in Language, and is recorded as handling language modeling, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.