BERT-Large-CAS (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
- Amazon
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 April 2019
- Authors
- Chenguang Wang, Mu Li, Alexander J. Smola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Neural Architecture Search - NAS, Language modeling
- Base model
- BERT-Large
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
- 340M
- Training data
- 103,000,000 tokens
- Epochs
- 50
Taken from Bert paper.
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 specified: https://github.com/cgraywang/gluon-nlp-1/tree/lmtransformer/scripts/language_model
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
- 139
- Benchmark data
- BERT-Large-CAS (WT103)
Sources
Where this record came from and when it was last checked.
- Reference
- Language Models with Transformers
- Last updated
- 25 May 2026
What the numbers mean
About this model
BERT-Large-CAS (WT103) was published by Amazon, in United States of America, in April 2019. It comes out of industry.
It works in Language, and is recorded as doing neural Architecture Search - NAS, Language modeling.
Its starting point was BERT-Large — most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 103,000,000 tokens.
Answers
BERT-Large-CAS (WT103) — common questions
Is BERT-Large-CAS (WT103) open source?
No. BERT-Large-CAS (WT103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does BERT-Large-CAS (WT103) have?
BERT-Large-CAS (WT103) has 340M parameters. Taken from Bert paper. 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 BERT-Large-CAS (WT103)?
BERT-Large-CAS (WT103) was published by Amazon, based in United States of America, categorised as industry.
When was BERT-Large-CAS (WT103) released?
BERT-Large-CAS (WT103) was published in April 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 BERT-Large-CAS (WT103) used for?
BERT-Large-CAS (WT103) works in Language, and is recorded as handling neural Architecture Search - NAS, Language modeling. 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 BERT-Large-CAS (WT103)?
None. BERT-Large-CAS (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.