CodonMPNN
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Harvard Medical School,Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 25 September 2024
- Authors
- Hannes Stark, Umesh Padia, Julia Balla, Cameron Diao, George Church
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Codon design
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
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license https://github.com/HannesStark/CodonMPNN
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- CodonMPNN for Organism Specific and Codon Optimal Inverse Folding
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
CodonMPNN was published by Harvard Medical School,Massachusetts Institute of Technology (MIT), in United States of America, in September 2024. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing codon design.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
CodonMPNN — common questions
What GPU do I need to run CodonMPNN?
We cannot say. CodonMPNN has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is CodonMPNN open source?
Its weights are published, so CodonMPNN can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does CodonMPNN have?
No parameter count has been published for CodonMPNN, which is why no memory or speed figure appears on this page.
Who created CodonMPNN?
CodonMPNN was published by Harvard Medical School,Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia,Academia.
When was CodonMPNN released?
CodonMPNN 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 CodonMPNN used for?
CodonMPNN works in Biology, and is recorded as handling codon design. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download CodonMPNN?
The weights for CodonMPNN are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.