RNADiffFold
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
- Hangzhou Institute of Medicine,Zhejiang University (ZJU),University of Chinese Academy of Sciences
- Organisation type
- Academia,Academia
- Country
- China
- Published
- 13 October 2024
- Authors
- Zhen Wang, Yizhen Feng, Qingwen Tian, Ziqi Liu, Pengju Yan, Xiaolin Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- RNA structure 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
- Epochs
- 400
RNAStrAlign (30,451) + bpRNA TR0 (102,318) + Mutate-seq (2,717) = 135,486 unique sequences 30,451 + 102,318 + 2,717 = 135,486 Final result: 135,486 (1.35e5)
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.
- How it was established
- Hardware
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 A40 PCIe
- Chips used
- 1
- Power draw
- 325 W
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
The code to reproduce our experiments and source data is available at https://github.com/HIM-AIM/RNADiffFold under an MIT License.
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
- 10
Sources
Where this record came from and when it was last checked.
- Reference
- RNADiffFold: Generative RNA Secondary Structure Prediction using Discrete Diffusion Models
- Last updated
- 1 January 2026
What the numbers mean
About this model
RNADiffFold was published by Hangzhou Institute of Medicine,Zhejiang University (ZJU),University of Chinese Academy of Sciences, in the country recorded as China, during October 2024. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of rNA structure prediction.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
RNADiffFold — common questions
RNADiffFold— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
RNADiffFold— what GPU do I need to run it?
We cannot say. It 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.
RNADiffFold— is it open source?
Its weights are published, so it 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.
RNADiffFold— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
RNADiffFold— who created it?
It was published by Hangzhou Institute of Medicine,Zhejiang University (ZJU),University of Chinese Academy of Sciences, based in China, an organisation categorised as academia,Academia.
RNADiffFold— when was it released?
It was published in October 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.
RNADiffFold— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of rNA structure prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.