AlphaTensor
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 5 October 2022
- Authors
- Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis & Pushmeet Kohli
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other, Games, Mathematics
- Approach
- Supervised
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
"We create a dataset containing 5 million such tensor-factorization pairs."
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.
- Training compute
- 7.1 × 10²⁰ FLOP
- How it was established
- Hardware
Compute: 0.3 [assumed utilization] * (64 cores / 2 cores per chip )*123000000000000 FLOP / TPU v3 chip / sec * 7 days * 24 hours / day * 3600 sec / hour = 7.1414784e+20 FLOP
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
- Google TPU v3
- Chips used
- 64
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 57.6 kW
In practice, the procedure takes a week to converge.
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
Apache 2.0 for algorithmic outputs https://github.com/google-deepmind/alphatensor
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
- SOTA improvement
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Discovering faster matrix multiplication algorithms with reinforcement learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
AlphaTensor was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2022. The category the publisher falls under is industry.
It works in the domain of Other, Games, Mathematics.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took a computation budget of roughly 7.1 × 10²⁰ FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
AlphaTensor — common questions
AlphaTensor— what is it used for?
It works in the domain of Other, Games, Mathematics. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
AlphaTensor— how much compute was used to train it?
Training consumed around 7.1 × 10²⁰ FLOP, on hardware recorded as Google TPU v3. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
AlphaTensor— 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.
AlphaTensor— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AlphaTensor— 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.
AlphaTensor— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
AlphaTensor— when was it released?
It was published in October 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.
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.