ProteinSetTransformer
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 Wisconsin Madison
- Organisation type
- Academia
- Country
- United States of America
- Published
- 23 September 2024
- Authors
- Karthik Anantharaman, Cody Martin, Anthony Gitter
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
datapoints = 6,391,562 proteins * 1 pass = 6.391562e6 This was from training the viral Protein Set Transformer (vPST) model on a dataset of 103,589 viruses containing 6,391,562 proteins total, with each protein counting as one datapoint.
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
- 1.7 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA A100 80GB (3.12 x 10^14 FLOP/s) 2. Training duration: - pst-large: 33.7 hours (directly provided) - pst-small: 3.15 hours (estimated based on parameter ratio 178M/5M) 3. Utilization rate: 40% 4. Calculation: pst-large: 3.12 x 10^14 × 121,320 × 0.40 = 1.51 x 10^19 FLOPs pst-small: 3.12 x 10^14 × 11,340 × 0.40 = 1.42 x 10^18 FLOPs Total: 1.65 x 10^19 FLOPs
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 A100
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
- Protein Set Transformer: A protein-based genome language model to power high diversity viromics
- Last updated
- 28 November 2025
What the numbers mean
Background
ProteinSetTransformer was published by University of Wisconsin Madison, in United States of America, in September 2024. 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.
Training and provenance
Training it took roughly 1.7 × 10¹⁹ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
Answers
ProteinSetTransformer — common questions
When was ProteinSetTransformer released?
ProteinSetTransformer 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 ProteinSetTransformer used for?
ProteinSetTransformer 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.
How much compute was used to train ProteinSetTransformer?
Around 1.7 × 10¹⁹ FLOP, on NVIDIA A100. 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.
What GPU do I need to run ProteinSetTransformer?
None. ProteinSetTransformer 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 ProteinSetTransformer open source?
The licensing for ProteinSetTransformer 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 ProteinSetTransformer have?
No parameter count has been published for ProteinSetTransformer, which is why no memory or speed figure appears on this page.
Who created ProteinSetTransformer?
ProteinSetTransformer was published by University of Wisconsin Madison, based in United States of America, categorised as academia.
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.