Vespa
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
- Organisation type
- Academia
- Country
- Germany
- Published
- 30 December 2021
- Authors
- Céline Marquet, Michael Heinzinger, Tobias Olenyi, Christian Dallago, Kyra Erckert, Michael Bernhofer, Dmitrii Nechaev & Burkhard Rost
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein or nucleotide language model (pLM/nLM)
- Approach
- Supervised
- Base model
- ProteinBERT
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
- 231K
- Training data
- tokens
"(3) standard convolutional neural network (CNN; with two convolutional layers with a window size of 7, connected through ReLU activations; dropout rate of 0.25; 231 k free parameters)"
ConSurf10k: 2.7 x 10^6 samples Eff10k: 1.00737 x 10^5 samples Total = 2.7 x 10^6 + 1.00737 x 10^5 = 2.800737 x 10^6 ≈ 2.8 x 10^6 datapoints
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
- Embeddings from protein language models predict conservation and variant effects
- Last updated
- 28 November 2025
What the numbers mean
About this model
Vespa was published by Technical University of Munich, in the country recorded as Germany, during December 2021. It comes out of an organisation categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein or nucleotide language model (pLM/nLM).
It builds on ProteinBERT. Most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Vespa — common questions
Vespa— 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.
Vespa— how many parameters does it have?
It has a parameter count of 231K. "(3) standard convolutional neural network (CNN; with two convolutional layers with a window size of 7, connected through ReLU activations; dropout rate of 0.25; 231 k free 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.
Vespa— who created it?
It was published by Technical University of Munich, based in Germany, an organisation categorised as academia.
Vespa— when was it released?
It was published in December 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.
Vespa— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Vespa— 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.
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.