RNAdiffusion
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Princeton University,Tsinghua University,Stanford University
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America, China
- Published
- 15 September 2024
- Authors
- Kaixuan Huang, Yukang Yang, Kaidi Fu, Yanyi Chu, Le Cong, Mengdi Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- RNA sequence generation
- Base model
- RNA-FM
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
Diffusion part: 1.1M sequences. 205k UTR sequences Reward model: 101k sequences Total: 1100000+205000+101000=1406000 sequences Assuming average token length of 300: 1406000*300=421800000
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
- 2.5 × 10¹⁹ FLOP
- How it was established
- Hardware
Autoencoder: 8.5h per epoch on single H100, 2 epochs total Diffusion model: 3.5h per epoch on single H100, 3 epochs total Reward model: 7h on a single A100 Total training time: 2*8.5+3*3.5+7=34.5 Compute: ((2*8.5+3*3.5)*756000000000000+7*312000000000000)*60*60*0.3=2.481192e+19
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 H100 PCIe,NVIDIA A100
- Chips used
- 1
- Wall-clock time
- 35 hours
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 9
Sources
Where this record came from and when it was last checked.
- Reference
- Latent Diffusion Models for Controllable RNA Sequence Generation
- Last updated
- 25 May 2026
What the numbers mean
What this model is
RNAdiffusion was published by Princeton University,Tsinghua University,Stanford University, in the country recorded as United States of America, during September 2024. It comes out of an organisation categorised as academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of rNA sequence generation.
Rather than being trained from scratch, it is derived from RNA-FM. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took a computation budget of roughly 2.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA H100 PCIe,NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
RNAdiffusion — common questions
RNAdiffusion— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of rNA sequence generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
RNAdiffusion— how much compute was used to train it?
Training consumed around 2.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA H100 PCIe,NVIDIA A100. 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.
RNAdiffusion— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
RNAdiffusion— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
RNAdiffusion— 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.
RNAdiffusion— who created it?
It was published by Princeton University,Tsinghua University,Stanford University, based in United States of America, an organisation categorised as academia,Academia,Academia.
RNAdiffusion— when was it released?
It 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.
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.