RiboDiffusion
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
- Beihang University,Nanjing University,Chinese University of Hong Kong (CUHK)
- Organisation type
- Academia,Academia,Academia
- Country
- China, Hong Kong
- Published
- 28 June 2024
- Authors
- Han Huang, Ziqian Lin, Dongchen He, Liang Hong, Yu Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- RNA 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
24,322 RNA chains × 150 nucleotides per chain = 3,648,300 data points ≈ 3.648 × 10⁶ (7,322 + 17,000 = 24,322 RNA chains)
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
- Unreleased
MIT license (the repo seems to include checkpoints and inference code only) https://github.com/ml4bio/RiboDiffusion
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
- 23
Sources
Where this record came from and when it was last checked.
- Reference
- RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models
- Last updated
- 1 January 2026
What the numbers mean
Background
RiboDiffusion was published by Beihang University,Nanjing University,Chinese University of Hong Kong (CUHK), in China, in June 2024. academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing rNA design.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
RiboDiffusion — common questions
When was RiboDiffusion released?
RiboDiffusion was published in June 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 RiboDiffusion used for?
RiboDiffusion works in Biology, and is recorded as handling rNA design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download RiboDiffusion?
The weights for RiboDiffusion 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 RiboDiffusion?
We cannot say. RiboDiffusion 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 RiboDiffusion open source?
Its weights are published, so RiboDiffusion 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 RiboDiffusion have?
No parameter count has been published for RiboDiffusion, which is why no memory or speed figure appears on this page.
Who created RiboDiffusion?
RiboDiffusion was published by Beihang University,Nanjing University,Chinese University of Hong Kong (CUHK), based in China, 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.