RoseTTAFold All-Atom (RFAA)
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
- University of Washington,Seoul National University,University of Sheffield
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America, Korea (Republic of), United Kingdom of Great Britain and Northern Ireland
- Published
- 9 October 2023
- Authors
- Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A Sutherland, Andrew Hitchcock, C Neil Hunter, Minkyung Baek, Frank DiMaio, David Baker
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Proteins
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
- 63,240,960 tokens
Datasets: 121,800 + 112,546 + 12,689 = 247,035 "All examples were cropped to have 256 tokens during the initial stages of training and 375 tokens during fine-tuning." 247035 * 256 =63,240,960 tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.1 × 10²⁰ FLOP
- How it was established
- Hardware
Supplementary material: "This took 8 days on 8 NVIDIA A6000 GPUs." 8 GPUS *3.87E+13 FLOPS/A6000 *60 * 60 * 24 * 8 days
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 RTX A6000
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-like license. inference code + training hyperparams: https://github.com/baker-laboratory/RoseTTAFold-All-Atom?tab=License-1-ov-file#readme
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
- Last updated
- 11 February 2026
What the numbers mean
What this model is
RoseTTAFold All-Atom (RFAA) was published by University of Washington,Seoul National University,University of Sheffield, in United States of America, in October 2023. academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein folding prediction, Proteins.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Training and provenance
Producing it required around 2.1 × 10²⁰ FLOP of arithmetic, on NVIDIA RTX A6000, which is a statement about the training budget rather than about inference.
The training set ran to roughly 63,240,960 tokens.
Its inclusion criterion is sOTA improvement.
Answers
RoseTTAFold All-Atom (RFAA) — common questions
What is RoseTTAFold All-Atom (RFAA) used for?
RoseTTAFold All-Atom (RFAA) works in Biology, and is recorded as handling protein folding prediction, Proteins. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download RoseTTAFold All-Atom (RFAA)?
The weights for RoseTTAFold All-Atom (RFAA) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train RoseTTAFold All-Atom (RFAA)?
Around 2.1 × 10²⁰ FLOP, on NVIDIA RTX A6000. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run RoseTTAFold All-Atom (RFAA)?
We cannot say. RoseTTAFold All-Atom (RFAA) 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 RoseTTAFold All-Atom (RFAA) open source?
Its weights are published, so RoseTTAFold All-Atom (RFAA) 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 RoseTTAFold All-Atom (RFAA) have?
No parameter count has been published for RoseTTAFold All-Atom (RFAA), which is why no memory or speed figure appears on this page.
Who created RoseTTAFold All-Atom (RFAA)?
RoseTTAFold All-Atom (RFAA) was published by University of Washington,Seoul National University,University of Sheffield, based in United States of America, categorised as academia,Academia,Academia.
When was RoseTTAFold All-Atom (RFAA) released?
RoseTTAFold All-Atom (RFAA) was published in October 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.
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.