GearNet
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,University of Cambridge,IBM Research,HEC Montreal,CIFAR AI Research
- Organisation type
- Academia,Academia,Academia,Industry,Academia,Research collective
- Country
- Canada, United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 1 November 2022
- Authors
- Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein function prediction, Protein fold classification
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
- 805,000 tokens
- Epochs
- 50
805,000 proteins × 300 residues/protein = 241,500,000 datapoints (2.415 × 10^8) Breakdown: 1. Initial protein count: 365,000 + 440,000 = 805,000 2. Final calculation: 805,000 × 300 = 241,500,000
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
- Chips used
- 4
- Power draw
- 3.2 kW
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
- 312
Sources
Where this record came from and when it was last checked.
- Reference
- Protein Representation Learning by Geometric Structure Pretraining
- Last updated
- 25 May 2026
What the numbers mean
About this model
GearNet was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,University of Cambridge,IBM Research,HEC Montreal,CIFAR AI Research, in Canada, in November 2022. The organisation is categorised as academia,Academia,Academia,Industry,Academia,Research collective.
It works in Biology, and is recorded as doing proteins, Protein function prediction, Protein fold classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Around 805,000 tokens went into training it.
Answers
GearNet — common questions
How many parameters does GearNet have?
No parameter count has been published for GearNet, which is why no memory or speed figure appears on this page.
Who created GearNet?
GearNet was published by Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),University of Montreal / Université de Montréal,University of Cambridge,IBM Research,HEC Montreal,CIFAR AI Research, based in Canada, categorised as academia,Academia,Academia,Industry,Academia,Research collective.
When was GearNet released?
GearNet was published in November 2022. 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 GearNet used for?
GearNet works in Biology, and is recorded as handling proteins, Protein function prediction, Protein fold classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run GearNet?
None. GearNet 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 GearNet open source?
The licensing for GearNet was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.