Local Transformer (WT103)
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
- Google Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 March 2020
- Authors
- Aurko Roy, Mohammad Saffar, Ashish Vaswani, David Grangier
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 103,000,000 tokens
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
- Google TPU v3
- Chips used
- 64
- Power draw
- 58.9 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 source
code (Apache): https://github.com/google-research/google-research/blob/master/routing_transformer/problems/wikitext103.py
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
- 754
- Benchmark data
- Local Transformer
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Content-Based Sparse Attention with Routing Transformers
- Last updated
- 25 May 2026
What the numbers mean
About this model
Local Transformer (WT103) was published by Google Research, in United States of America, in March 2020. It comes out of industry.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Around 103,000,000 tokens went into training it.
Answers
Local Transformer (WT103) — common questions
Is Local Transformer (WT103) open source?
No. Local Transformer (WT103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Local Transformer (WT103) have?
No parameter count has been published for Local Transformer (WT103), which is why no memory or speed figure appears on this page.
Who created Local Transformer (WT103)?
Local Transformer (WT103) was published by Google Research, based in United States of America, categorised as industry.
When was Local Transformer (WT103) released?
Local Transformer (WT103) was published in March 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.
What is Local Transformer (WT103) used for?
Local Transformer (WT103) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Local Transformer (WT103)?
None. Local Transformer (WT103) 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.
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.