Pro-PRIME
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
- Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology,Shanghai Tech University,Guangzhou Inernational Bio Island,Chinese Academy of Sciences,Shanghai Academy of Experimental Medicine
- Organisation type
- Academia,Academia,Academia,Academia,Academia
- Country
- China
- Published
- 28 October 2024
- Authors
- Fan Jiang, Mingchen Li, Jiajun Dong, Yuanxi Yu, Xinyu Sun, Banghao Wu, Jin Huang, Liqi Kang, Yufeng Pei, Liang Zhang, Shaojie Wang, Wenxue Xu, Jingyao Xin, Wanli Ouyang, Guisheng Fan, Lirong Zheng, Yang Tan, Zhiqiang Hu, Yi Xiong, Yan Feng, Guangyu Yang, Qian Liu, Jie Song, Jia Liu, Liang Hong, Pan Tan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
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
- 650M
- Training data
- tokens
- Epochs
- 7
96,000,000 sequences × 300 residues/sequence = 28,800,000,000 tokens (2.88 × 10¹⁰)
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
- 8.2 × 10²⁰ FLOP
- How it was established
- Hardware
Tokens per step; 4096*8*32=1048576 Total training tokens 1048576*200000=209715200000 Epochs: 209715200000 / 28800000001 = 7.28 FLOP: 6*650000000*209715200000=8.1788928e+20
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
- 1
Sources
Where this record came from and when it was last checked.
- Reference
- Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
- Last updated
- 28 November 2025
What the numbers mean
Background
Pro-PRIME was published by Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology,Shanghai Tech University,Guangzhou Inernational Bio Island,Chinese Academy of Sciences,Shanghai Academy of Experimental Medicine, in the country recorded as China, during October 2024. It comes out of an organisation categorised as academia,Academia,Academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein design.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 8.2 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Pro-PRIME — common questions
Pro-PRIME— how much compute was used to train it?
Training consumed around 8.2 × 10²⁰ 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.
Pro-PRIME— 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.
Pro-PRIME— 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.
Pro-PRIME— how many parameters does it have?
It has a parameter count of 650M. 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.
Pro-PRIME— who created it?
It was published by Shanghai Jiao Tong University,Shanghai AI Lab,East China University of Science and Technology,Shanghai Tech University,Guangzhou Inernational Bio Island,Chinese Academy of Sciences,Shanghai Academy of Experimental Medicine, based in China, an organisation categorised as academia,Academia,Academia,Academia,Academia.
Pro-PRIME— when was it released?
It was published in October 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.
Pro-PRIME— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.