ProteinGAN
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
- Vilnius University,Chalmers University of Technology
- Organisation type
- Academia,Academia
- Country
- Lithuania, Sweden
- Published
- 4 March 2021
- Authors
- Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist & Aleksej Zelezniak
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein generation
- Approach
- Unsupervised
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
- 60M
- Training data
- tokens
"The final architecture of the network comprised 45 layers with over 60 million trainable parameters"
16,706 × 319 = 5,329,214 tokens Total datapoints = 5.33 million tokens
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
- 4.3 × 10¹⁸ FLOP
- How it was established
- Hardware
2.5 million steps, batch size 64, 210 hours on NVIDIA Tesla P100 system
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 P100
- Chips used
- 1
- Chip-hours
- 210
- Wall-clock time
- 210 hours (8.8 days)
- Power draw
- 279 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
- Expanding functional protein sequence spaces using generative adversarial networks
- Last updated
- 28 November 2025
What the numbers mean
Background
ProteinGAN was published by Vilnius University,Chalmers University of Technology, in the country recorded as Lithuania, during March 2021. The category the publisher falls under is academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein generation.
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
Producing it required arithmetic totalling around 4.3 × 10¹⁸ FLOP, on hardware recorded as NVIDIA P100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
ProteinGAN — common questions
ProteinGAN— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
ProteinGAN— how much compute was used to train it?
Training consumed around 4.3 × 10¹⁸ FLOP, on hardware recorded as NVIDIA P100. 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.
ProteinGAN— 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.
ProteinGAN— 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.
ProteinGAN— how many parameters does it have?
It has a parameter count of 60M. "The final architecture of the network comprised 45 layers with over 60 million trainable 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.
ProteinGAN— who created it?
It was published by Vilnius University,Chalmers University of Technology, based in Lithuania, an organisation categorised as academia,Academia.
ProteinGAN— when was it released?
It was published in March 2021. 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.