VespaG
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
- Technical University of Munich,Sorbonne University,Institute for Advanced Study,Université Paris Cité,Institut Universitaire de France (IUF)
- Organisation type
- Academia,Academia,Academia,Academia,Research collective
- Country
- Germany, France, United States of America
- Published
- 9 September 2024
- Authors
- Céline Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein property prediction, Protein or nucleotide language model (pLM/nLM)
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
- 660K
- Training data
- tokens
- Epochs
- 200
Total datapoints = 39,000,000 39,000,000 = 3.9 x 10^7 Final data estimate: 3.9e7
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
- Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction
- Last updated
- 28 November 2025
What the numbers mean
Background
VespaG was published by Technical University of Munich,Sorbonne University,Institute for Advanced Study,Université Paris Cité,Institut Universitaire de France (IUF), in the country recorded as Germany, during September 2024. It comes out of an organisation categorised as academia,Academia,Academia,Academia,Research collective.
It works in the domain of Biology, and is recorded as performing the task of protein property prediction, Protein or nucleotide language model (pLM/nLM).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
VespaG — common questions
VespaG— 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.
VespaG— 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.
VespaG— how many parameters does it have?
It has a parameter count of 660K. 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.
VespaG— who created it?
It was published by Technical University of Munich,Sorbonne University,Institute for Advanced Study,Université Paris Cité,Institut Universitaire de France (IUF), based in Germany, an organisation categorised as academia,Academia,Academia,Academia,Research collective.
VespaG— 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.
VespaG— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein property prediction, Protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.