CPDiffusion
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,University of New South Wales,University of Cambridge,Shanghai AI Lab
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- China, Australia, United Kingdom of Great Britain and Northern Ireland
- Published
- 10 September 2024
- Authors
- Bingxin Zhou, Lirong Zheng, Banghao Wu, Kai Yi, Bozitao Zhong, Yang Tan, Qian Liu, Pietro Liò, Liang Hong
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
- 4M
- Training data
- 6,207,900 tokens
CPDiffusion trains a denoising diffusion model with 4 million learnable parameters from natural protein structures.
CATH: 20,000 × 250 = 5,000,000 pAgo: 694 × 800 = 555,200 Total: 5,000,000 + 555,200 = 5,555,200 ≈ 5.6 × 10^6 datapoints
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
- A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity
- Last updated
- 28 November 2025
What the numbers mean
About this model
CPDiffusion was published by Shanghai Jiao Tong University,University of New South Wales,University of Cambridge,Shanghai AI Lab, in China, in September 2024. It comes out of academia,Academia,Academia,Academia.
It works in Biology, and is recorded as doing protein design.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on about 6,207,900 tokens of text.
Answers
CPDiffusion — common questions
What is CPDiffusion used for?
CPDiffusion works in Biology, and is recorded as handling protein design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run CPDiffusion?
None. CPDiffusion 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 CPDiffusion open source?
The licensing for CPDiffusion 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 CPDiffusion have?
CPDiffusion has 4M parameters. CPDiffusion trains a denoising diffusion model with 4 million learnable parameters from natural protein structures. 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.
Who created CPDiffusion?
CPDiffusion was published by Shanghai Jiao Tong University,University of New South Wales,University of Cambridge,Shanghai AI Lab, based in China, categorised as academia,Academia,Academia,Academia.
When was CPDiffusion released?
CPDiffusion 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.
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.