AlphaProof
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
- 25 July 2024
- Authors
- AlphaProof development was led by Thomas Hubert, Rishi Mehta and Laurent Sartran AlphaProof was developed with key contributions from Hussain Masoom, Aja Huang, Miklós Z. Horváth, Tom Zahavy, Vivek Veeriah, Eric Wieser, Jessica Yung, Lei Yu, Yannick Schroecker, Julian Schrittwieser, Ottavia Bertolli, Borja Ibarz, Edward Lockhart, Edward Hughes, Mark Rowland, Grace Margand. Alex Davies and Daniel Z…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Mathematics
- Task
- Automated theorem proving, Quantitative reasoning
- Approach
- Reinforcement learning
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
- 3B
- Training data
- tokens
"Total Parameters: 3B" from Supplemental Data Table 2: Prover network architecture hyperparameters.
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.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- AlphaProof, a new reinforcement-learning based system for formal math reasoning
- Last updated
- 10 March 2026
What the numbers mean
Where it came from
AlphaProof was published by Google DeepMind, in United States of America, in July 2024. It comes out of industry.
It works in Mathematics, and is recorded as doing automated theorem proving, Quantitative reasoning.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
AlphaProof — common questions
When was AlphaProof released?
AlphaProof was published in July 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.
What is AlphaProof used for?
AlphaProof works in Mathematics, and is recorded as handling automated theorem proving, Quantitative reasoning. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run AlphaProof?
None. AlphaProof 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 AlphaProof open source?
No. AlphaProof has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AlphaProof have?
AlphaProof has 3B parameters. "Total Parameters: 3B" from Supplemental Data Table 2: Prover network architecture hyperparameters. 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 AlphaProof?
AlphaProof was published by Google DeepMind, based in United States of America, categorised as industry.
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.