AlphaGo Master
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
- 19 October 2017
- Authors
- D Silver, J Schrittwieser, K Simonyan, I Antonoglou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Go
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
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
- 3.4 × 10²⁰ FLOP
- How it was established
- Benchmarks
This is a guess. There was no single journal publication that accompanied this model, that gave information about architecture/model training time etc. All I could find was that it has the same architecture as AlphaGo Zero, and that it had roughly the same power consumption as AGZ. See for instance: https://deepmind.com/blog/article/alphago-zero-starting-scratch Since AGZ reaches the ELO of AlphaGo Master in about 25-30 days (60-75% of the total training time), I estimate the compute to be aro…
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 v1
- Wall-clock time
- 72 hours
- Compute cost
- $471,445
"Training started from completely random behaviour and continued without human intervention for approximately three days."
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
- Highly cited
- Record confidence
- Likely
- Citations
- 10,021
Sources
Where this record came from and when it was last checked.
- Reference
- Mastering the game of Go without human knowledge
- Last updated
- 1 January 2026
What the numbers mean
What this model is
AlphaGo Master was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2017. The publishing organisation is categorised as industry.
It works in the domain of Games, and is recorded as performing the task of go.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 3.4 × 10²⁰ FLOP, on hardware recorded as Google TPU v1. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Its inclusion criterion: highly cited.
Answers
AlphaGo Master — common questions
AlphaGo Master— how much compute was used to train it?
Training consumed around 3.4 × 10²⁰ FLOP, on hardware recorded as Google TPU v1. 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.
AlphaGo Master— 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.
AlphaGo Master— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AlphaGo Master— 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.
AlphaGo Master— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
AlphaGo Master— when was it released?
It was published in October 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
AlphaGo Master— what is it used for?
It works in the domain of Games, and is recorded as handling the task of go. 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.
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.