Hanabi 4 player
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,University of Oxford,Carnegie Mellon University (CMU),Google Brain
- Organisation type
- Industry,Academia,Academia,Industry
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 1 February 2019
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Hanabi
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
- 764K
- Training data
- 20,000,000,000 tokens
source: https://docs.google.com/spreadsheets/d/1Kj4Q5WADcDXtUJLIOfGTCE3tGvxNczEMwyy8QtgSkHk/edit#gid=54587040&fvid=1361937389
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
- 4.3 × 10¹⁸ FLOP
- How it was established
- Hardware
14.13e+12 FLOP/s * 7 days * 86400 s/day * 0.50 utilization = 4.3e+18 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
- NVIDIA V100
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
- Historical significance
- Record confidence
- Confident
- Citations
- 229
Adapted some SOTA RL algorithms to a new task that posed research challenges
Sources
Where this record came from and when it was last checked.
- Reference
- The Hanabi Challenge: A New Frontier for AI Research
- Last updated
- 28 November 2025
What the numbers mean
About this model
Hanabi 4 player was published by DeepMind,University of Oxford,Carnegie Mellon University (CMU),Google Brain, in United Kingdom of Great Britain and Northern Ireland, in February 2019. industry,Academia,Academia,Industry is the category the publisher falls under.
It works in Games, and is recorded as doing hanabi.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 4.3 × 10¹⁸ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 20,000,000,000 tokens went into training it.
The reason it appears in this catalogue at all is historical significance.
Answers
Hanabi 4 player — common questions
How much compute was used to train Hanabi 4 player?
Around 4.3 × 10¹⁸ FLOP, on NVIDIA V100. 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 Hanabi 4 player?
None. Hanabi 4 player 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 Hanabi 4 player open source?
No. Hanabi 4 player has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Hanabi 4 player have?
Hanabi 4 player has 764K parameters. source: https://docs.google.com/spreadsheets/d/1Kj4Q5WADcDXtUJLIOfGTCE3tGvxNczEMwyy8QtgSkHk/edit#gid=54587040&fvid=1361937389. 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 Hanabi 4 player?
Hanabi 4 player was published by DeepMind,University of Oxford,Carnegie Mellon University (CMU),Google Brain, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia,Academia,Industry.
When was Hanabi 4 player released?
Hanabi 4 player was published in February 2019. 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 Hanabi 4 player used for?
Hanabi 4 player works in Games, and is recorded as handling hanabi. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.