EigenFold
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
- Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 5 April 2023
- Authors
- Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi Jaakkola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction
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
" To reduce training time, we train (and validate) only on structures with residue lengths between 20 and 256, for a total of 230,520 (14,128) training (validation) structures."
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/bjing2016/EigenFold
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
- EigenFold: Generative Protein Structure Prediction with Diffusion Models
- Last updated
- 28 November 2025
What the numbers mean
About this model
EigenFold was published by Massachusetts Institute of Technology (MIT), in United States of America, in April 2023. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein folding prediction.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
EigenFold — common questions
Is EigenFold open source?
Its weights are published, so EigenFold 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 EigenFold have?
No parameter count has been published for EigenFold, which is why no memory or speed figure appears on this page.
Who created EigenFold?
EigenFold was published by Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia.
When was EigenFold released?
EigenFold was published in April 2023. 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 EigenFold used for?
EigenFold works in Biology, and is recorded as handling protein folding prediction. 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.
Where can I download EigenFold?
The weights for EigenFold are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run EigenFold?
We cannot say. EigenFold 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.
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.