PepGLAD
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
- Tsinghua University,Renmin University of China
- Organisation type
- Academia,Academia
- Country
- China
- Published
- 21 February 2024
- Authors
- Xiangzhe Kong, Yinjun Jia, Wenbing Huang, Yang Liu
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.
- Training data
- tokens
- Epochs
- 560
PepBench Training: 4,157 entries × 10 residues = 41,570 residues PepBDB Training: 8,434 entries × 10 residues = 84,340 residues ProtFrag: 70,645 monomers × 1 residue = 70,645 residues Total: 41,570 + 84,340 + 70,645 = 196,555 residues
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.
- How it was established
- Hardware
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 1
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
- Full-Atom Peptide Design with Geometric Latent Diffusion
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PepGLAD was published by Tsinghua University,Renmin University of China, in the country recorded as China, during February 2024. The category the publisher falls under is 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.
Answers
PepGLAD — common questions
PepGLAD— who created it?
It was published by Tsinghua University,Renmin University of China, based in China, an organisation categorised as academia,Academia.
PepGLAD— when was it released?
It was published in February 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.
PepGLAD— 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.
PepGLAD— 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.
PepGLAD— 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.
PepGLAD— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
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.