CodonTransformer
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
- Vector Institute,University of Toronto,Université Paris Cité
- Organisation type
- Academia,Academia,Academia
- Country
- Canada, France
- Published
- 13 September 2024
- Authors
- Adibvafa Fallahpour, Vincent Gureghian, Guillaume J. Filion, Ariel B. Lindner, Amir Pandi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Codon optimization
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
- 89.6M
- Training data
- 45,053,865 tokens
- Epochs
- 20
"total number of parameters to 89.6 million."
1,001,197 sequences × 300 tokens/sequence = 300359100
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 V100
- Chips used
- 16
- Power draw
- 9.5 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- CodonTransformer: a multispecies codon optimizer using context-aware neural networks
- Last updated
- 28 November 2025
What the numbers mean
Background
CodonTransformer was published by Vector Institute,University of Toronto,Université Paris Cité, in Canada, in September 2024. The organisation is categorised as academia,Academia,Academia.
It works in Biology, and is recorded as doing codon optimization.
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 45,053,865 tokens.
Answers
CodonTransformer — common questions
When was CodonTransformer released?
CodonTransformer was published in September 2024. 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 CodonTransformer used for?
CodonTransformer works in Biology, and is recorded as handling codon optimization. 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 CodonTransformer?
None. CodonTransformer 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 CodonTransformer open source?
The licensing for CodonTransformer was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does CodonTransformer have?
CodonTransformer has 89.6M parameters. "total number of parameters to 89.6 million.". 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 CodonTransformer?
CodonTransformer was published by Vector Institute,University of Toronto,Université Paris Cité, based in Canada, categorised as academia,Academia,Academia.
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.