Machine learning a model for RNA structure prediction
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
- International School for Advanced Studies,Institute of Structural Biology,Technical University of Munich
- Organisation type
- Academia,Academia,Academia
- Country
- Italy, Germany
- Published
- 16 November 2020
- Authors
- Nicola Calonaci, Alisha Jones, Francesca Cuturello, Michael Sattler, Giovanni Bussi
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
(52 + 388) / 2 = 220 nucleotides per RNA 18 RNA molecules × 220 nucleotides = 3,960 datapoints Rounded to: 4,000 datapoints
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
- 1.8 × 10¹⁸ FLOP
- How it was established
- Hardware
1. Hardware setup: 576 CPUs (288 nodes × 2 CPUs/node), Intel Xeon E5-2683 v4 FLOP/s per CPU: 2.688 × 10¹¹ FLOP/s Total system: 1.548288 × 10¹⁴ FLOP/s 2. Training duration: Directly provided: 8 hours = 28,800 seconds 3. Utilization rate: 40% (0.4) 4. Final calculation: 1.548288 × 10¹⁴ FLOP/s × 28,800s × 0.4 = 1.78 × 10¹⁸ FLOPs
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
- 32
Sources
Where this record came from and when it was last checked.
- Reference
- Machine learning a model for RNA structure prediction
- Last updated
- 28 November 2025
What the numbers mean
About this model
Machine learning a model for RNA structure prediction was published by International School for Advanced Studies,Institute of Structural Biology,Technical University of Munich, in the country recorded as Italy, during November 2020. The publishing organisation is categorised as academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of rNA structure prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 1.8 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Machine learning a model for RNA structure prediction — common questions
Machine learning a model for RNA structure prediction— 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.
Machine learning a model for RNA structure prediction— how much compute was used to train it?
Training consumed around 1.8 × 10¹⁸ FLOP. 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.
Machine learning a model for RNA structure prediction— 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.
Machine learning a model for RNA structure prediction— 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.
Machine learning a model for RNA structure prediction— 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.
Machine learning a model for RNA structure prediction— who created it?
It was published by International School for Advanced Studies,Institute of Structural Biology,Technical University of Munich, based in Italy, an organisation categorised as academia,Academia,Academia.
Machine learning a model for RNA structure prediction— when was it released?
It was published in November 2020. 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.