AlphaProteo
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 September 2024
- Authors
- Vinicius Zambaldi, David La, Alexander E. Chu, Harshnira Patani, Amy E. Danson, Tristan O. C. Kwan, Thomas Frerix, Rosalia G. Schneider, David Saxton, Ashok Thillaisundaram, Zachary Wu, Isabel Moraes, Oskar Lange, Eliseo Papa, Gabriella Stanton, Victor Martin, Sukhdeep Singh, Lai H. Wong, Russ Bates, Simon A. Kohl, Josh Abramson, Andrew W. Senior, Yilmaz Alguel, Mary Y. Wu, Irene M. Aspalter, Kati…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein generation, Proteins
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
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Unknown
Economic impacts from development of commercially and socially valuable protein designs and materials
Sources
Where this record came from and when it was last checked.
- Reference
- De novo design of high-affinity protein binders with AlphaProteo
- Last updated
- 28 November 2025
What the numbers mean
Background
AlphaProteo was published by Google DeepMind, in United States of America, in September 2024. industry is the category the publisher falls under.
It works in Biology, and is recorded as doing protein generation, Proteins.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The reason it appears in this catalogue at all is historical significance.
Answers
AlphaProteo — common questions
What is AlphaProteo used for?
AlphaProteo works in Biology, and is recorded as handling protein generation, Proteins. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run AlphaProteo?
None. AlphaProteo 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 AlphaProteo open source?
No. AlphaProteo has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AlphaProteo have?
No parameter count has been published for AlphaProteo, which is why no memory or speed figure appears on this page.
Who created AlphaProteo?
AlphaProteo was published by Google DeepMind, based in United States of America, categorised as industry.
When was AlphaProteo released?
AlphaProteo 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.