ProtT3
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
- National University of Singapore,University of Science and Technology of China (USTC),Hokkaido University
- Organisation type
- Academia,Academia,Academia
- Country
- Singapore, China, Japan
- Published
- 21 May 2024
- Authors
- Zhiyuan Liu, An Zhang, Hao Fei, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein question answering
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
- 1.6B
- Training data
- tokens
- Epochs
- 60
Table 12
430,595 + 422,315 + 3,360,000 = 4,212,910 datapoints 4,212,910 × 300 = 1,263,873,000 tokens Final estimate: 1.3 billion tokens Exact numbers from Table 2 430595*(336+48)+422315*(338+101)+3359693*(291+10)=1362012358
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,NVIDIA V100
- Chips used
- 6
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
- 34
Sources
Where this record came from and when it was last checked.
- Reference
- ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
- Last updated
- 25 May 2026
What the numbers mean
What this model is
ProtT3 was published by National University of Singapore,University of Science and Technology of China (USTC),Hokkaido University, in the country recorded as Singapore, during May 2024. The category the publisher falls under is academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein question answering.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
ProtT3 — common questions
ProtT3— when was it released?
It was published in May 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.
ProtT3— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
ProtT3— 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.
ProtT3— 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.
ProtT3— how many parameters does it have?
It has a parameter count of 1.6B. Table 12. 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.
ProtT3— who created it?
It was published by National University of Singapore,University of Science and Technology of China (USTC),Hokkaido University, based in Singapore, an organisation categorised as academia,Academia,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.