Protein-Mamba
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
- Rensselaer Polytechnic Institute,Stanford University,University of Minnesota,Korea Advanced Institute of Science and Technology (KAIST),University of Illinois Urbana-Champaign (UIUC)
- Organisation type
- Academia,Academia,Academia,Academia,Academia
- Country
- United States of America, Korea (Republic of)
- Published
- 24 September 2024
- Authors
- Bohao Xu, Yingzhou Lu, Yoshitaka Inoue, Namkyeong Lee, Tianfan Fu, Jintai Chen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- 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.
- Training data
- tokens
- Epochs
- 150
Total number of datapoints = 27,043 proteins × 300 amino acids/protein = 8,112,900 This equals approximately 8.1 × 10⁶ datapoints 27,043 × 300 = 8,112,900
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.
- How it was established
- Hardware
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 3090
- Chips used
- 1
- Power draw
- 379 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
- Citations
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- Protein-Mamba: Biological Mamba Models for Protein Function Prediction
- Last updated
- 1 January 2026
What the numbers mean
About this model
Protein-Mamba was published by Rensselaer Polytechnic Institute,Stanford University,University of Minnesota,Korea Advanced Institute of Science and Technology (KAIST),University of Illinois Urbana-Champaign (UIUC), in United States of America, in September 2024. academia,Academia,Academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Protein-Mamba — common questions
What GPU do I need to run Protein-Mamba?
None. Protein-Mamba 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 Protein-Mamba open source?
The licensing for Protein-Mamba 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 Protein-Mamba have?
No parameter count has been published for Protein-Mamba, which is why no memory or speed figure appears on this page.
Who created Protein-Mamba?
Protein-Mamba was published by Rensselaer Polytechnic Institute,Stanford University,University of Minnesota,Korea Advanced Institute of Science and Technology (KAIST),University of Illinois Urbana-Champaign (UIUC), based in United States of America, categorised as academia,Academia,Academia,Academia,Academia.
When was Protein-Mamba released?
Protein-Mamba 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.
What is Protein-Mamba used for?
Protein-Mamba works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
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.