Student of Games
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
- 6 December 2021
- Authors
- Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Chess, Go, Poker
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
- 245,760,000,000 tokens
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
- 3.7 × 10²² FLOP
"We trained a version of AlphaZero using its original settings in chess and Go, e.g. , using 800 MCTS simulations during training, with 3500 concurrent actors each on a single TPUv4, for a total of 800k training steps. SOG was trained using a similar amount of TPU resources."
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
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 32
"Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard"
Sources
Where this record came from and when it was last checked.
- Reference
- Player of Games
- Last updated
- 25 May 2026
What the numbers mean
About this model
Student of Games was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during December 2021. It comes out of an organisation categorised as industry.
It works in the domain of Games, and is recorded as performing the task of chess, Go, Poker.
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 3.7 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 245,760,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Student of Games — common questions
Student of Games— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Student of Games— 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.
Student of Games— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
Student of Games— when was it released?
It was published in December 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Student of Games— what is it used for?
It works in the domain of Games, and is recorded as handling the task of chess, Go, Poker. 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.
Student of Games— how much compute was used to train it?
Training consumed around 3.7 × 10²² FLOP. 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.
Student of Games— 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.
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.