ConfRank
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
- University of Bonn,Institute for Numerical Simulation,Fraunhofer Institute for Algorithms and Scientific Computing
- Organisation type
- Academia,Academia
- Country
- Germany
- Published
- 24 November 2024
- Authors
- Christian Hölzer, Rick Oerderm, Stefan Grimme, Jan Hamaekers,
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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.
- Parameters
- 150K
- Training data
- 1,396,310 tokens
7,349 molecular ensembles × 20 conformers = 146,980 conformers Pairs per ensemble = (20 × 19) ÷ 2 = 190 pairs Total pairs = 7,349 ensembles × 190 pairs = 1,396,310 datapoints
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 GeForce RTX 3070
- Chips used
- 1
- Power draw
- 238 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training
- Last updated
- 28 November 2025
What the numbers mean
About this model
ConfRank was published by University of Bonn,Institute for Numerical Simulation,Fraunhofer Institute for Algorithms and Scientific Computing, in Germany, in November 2024. It comes out of academia,Academia.
It works in Biology, and is recorded as doing drug discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It was trained on about 1,396,310 tokens of text.
Answers
ConfRank — common questions
Who created ConfRank?
ConfRank was published by University of Bonn,Institute for Numerical Simulation,Fraunhofer Institute for Algorithms and Scientific Computing, based in Germany, categorised as academia,Academia.
When was ConfRank released?
ConfRank was published in November 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 ConfRank used for?
ConfRank works in Biology, and is recorded as handling drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run ConfRank?
None. ConfRank 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.
Is ConfRank open source?
The licensing for ConfRank was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ConfRank have?
ConfRank has 150K parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.