Protst
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
- Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,Intel Labs,HEC Montreal,CIFAR AI Research
- Organisation type
- Academia,Academia,Industry,Academia,Research collective
- Country
- Canada, United States of America
- Published
- 28 January 2023
- Authors
- Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
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
Total Datapoints = (553,052 pairs × 450 residues/pair) + (553,052 pairs × 50 tokens/pair) = 248,873,400 + 27,652,600 = 276,526,000 (2.765e+8)
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
- 1.4 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 4x NVIDIA Tesla V100 GPUs (1.30 x 10^14 FLOP/s per GPU) 2. Training duration: 69,131 seconds (≈19.2 hours) - estimated from 20 epochs, 553,052 samples, batch size 16, assuming 0.1s per step 3. Utilization rate: 40% 4. Final calculation: 1.30 x 10^14 FLOP/s × 4 GPUs × 69,131s × 0.4 = 1.44 x 10^19 FLOPs
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 124
Sources
Where this record came from and when it was last checked.
- Reference
- ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
- Last updated
- 1 January 2026
What the numbers mean
What this model is
Protst was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,Intel Labs,HEC Montreal,CIFAR AI Research, in Canada, in January 2023. The organisation is categorised as academia,Academia,Industry,Academia,Research collective.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 1.4 × 10¹⁹ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Protst — common questions
When was Protst released?
Protst was published in January 2023. 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 Protst used for?
Protst works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Protst?
Around 1.4 × 10¹⁹ FLOP. 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 Protst?
None. Protst 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 Protst open source?
The licensing for Protst 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 Protst have?
No parameter count has been published for Protst, which is why no memory or speed figure appears on this page.
Who created Protst?
Protst was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,Intel Labs,HEC Montreal,CIFAR AI Research, based in Canada, categorised as academia,Academia,Industry,Academia,Research collective.
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.