AlphaGo Lee
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
- 27 January 2016
- Authors
- David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
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
- 300,000,000 tokens
We trained the policy network pσ to classify positions according to expert moves played in the KGS data set. This data set contains 29.4 million positions from 160,000 games played by KGS 6 to 9 dan human players; 35.4% of the games are handicap games.
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
- 1.9 × 10²¹ FLOP
- How it was established
- Comparison with other models
This number is pretty uncertain. I expect it to be right to around a factor of 3, at least compared to AlphaGo Fan. The architecture used was pretty much the same as AlphaGo Fan, but it was "trained for longer" and had around 5.33x the number of convolutional layers of AlphaGo Fan (256/48 = 5.33). The convolutional layers are the major contributor to the training compute, so I somewhat arbitrarily just multiply the compute for AlphaGo Fan by 5. Thus 3.8e20 * 5 = 1.9e21 Otherwise there has be…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 696 hours (29 days)
- Compute cost
- $22,207
Training times are given for several components: - Policy network classifier: 3 weeks - Policy network RL: 1 day - Value network regression: 1 week - Rollout policy: "Similar to the policy network, the weights π of the rollout policy are trained from 8 million positions from human games on the Tygem server to maximize log likelihood by stochastic gradient descent. Rollouts execute at approximately 1,000 simulations per second per CPU thread on an empty board." could suggest (8M sims / 1000 sims/…
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.
- Frontier model
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Speculative
- Citations
- 18,175
Sources
Where this record came from and when it was last checked.
- Reference
- Mastering the game of Go with deep neural networks and tree search
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
AlphaGo Lee was published by DeepMind, in United Kingdom of Great Britain and Northern Ireland, in January 2016. The organisation is categorised as industry.
It works in Games, and is recorded as doing go.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took roughly 1.9 × 10²¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 300,000,000 tokens of text.
The reason it appears in this catalogue at all is highly cited.
Answers
AlphaGo Lee — common questions
When was AlphaGo Lee released?
AlphaGo Lee was published in January 2016. 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 AlphaGo Lee used for?
AlphaGo Lee works in Games, and is recorded as handling go. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train AlphaGo Lee?
Around 1.9 × 10²¹ 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.
What GPU do I need to run AlphaGo Lee?
None. AlphaGo Lee 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 AlphaGo Lee open source?
No. AlphaGo Lee has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AlphaGo Lee have?
No parameter count has been published for AlphaGo Lee, which is why no memory or speed figure appears on this page.
Who created AlphaGo Lee?
AlphaGo Lee was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, 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.