Tensorized Transformer (257M)
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/generation
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
- 257M
- Training data
- 103,000,000 tokens
- Epochs
- 30
257M (Table 5)
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
- 4.8 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 257000000 parameters * 103000000 tokens * 30 epochs = 4.76478e+18 FLOP
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
- Open (non-commercial)
code, no license: https://github.com/szhangtju/The-compression-of-Transformer
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 (257M)
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
Background
Tensorized Transformer (257M) was published by Tianjin University,Microsoft Research Asia,Beijing Institute of Technology, in China, in June 2019. The organisation is categorised as academia,Industry,Academia.
It works in Language, and is recorded as doing language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 4.8 × 10¹⁸ FLOP, on NVIDIA P40. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 103,000,000 tokens went into training it.
Answers
Tensorized Transformer (257M) — common questions
How much compute was used to train Tensorized Transformer (257M)?
Around 4.8 × 10¹⁸ FLOP, on NVIDIA P40. 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.
What GPU do I need to run Tensorized Transformer (257M)?
None. Tensorized Transformer (257M) 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 (257M) open source?
No. Tensorized Transformer (257M) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Tensorized Transformer (257M) have?
Tensorized Transformer (257M) has 257M parameters. 257M (Table 5). 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.
Who created Tensorized Transformer (257M)?
Tensorized Transformer (257M) was published by Tianjin University,Microsoft Research Asia,Beijing Institute of Technology, based in China, categorised as academia,Industry,Academia.
When was Tensorized Transformer (257M) released?
Tensorized Transformer (257M) 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 (257M) used for?
Tensorized Transformer (257M) works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.