Pluribus
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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 11 July 2019
- Authors
- Noam Brown, Tuomas Sandholm
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- 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
- 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
- 6.6 × 10¹⁶ FLOP
- How it was established
- Hardware
Trained in 8 days on a 64 core CPU https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/ "We trained the blueprint strategy for Pluribus in eight days on a 64-core server and required less than 512 GB of RAM. No GPUs were used. At typical cloud computing instance rates, it would cost less than $150 to train." Guess: trained on i7 Intel CPU, approx 5e9 FLOP/s for each core. https://epoch.ai/blog/estimating-training-compute 8 days, 64 cores, 5e9 FLOP/s, 30% utilization
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
- Likely
- Citations
- 797
first to beat humans at multiplayer poker: "Developing a superhuman AI for multiplayer poker was the widely,recognized main remaining milestone. In this paper we describe Pluribus, an AI capable of defeating elite human professionals in six-player no-limit Texas hold’em poker, the most commonly played poker format in the world."
Sources
Where this record came from and when it was last checked.
- Reference
- Superhuman AI for multiplayer poker
- Last updated
- 1 January 2026
What the numbers mean
What this model is
Pluribus was published by Facebook AI Research, in the country recorded as United States of America, during July 2019. The publishing organisation is categorised as industry.
It works in the domain of Games, and is recorded as performing the task of 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
The training run consumed about 6.6 × 10¹⁶ FLOP. 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
Pluribus — common questions
Pluribus— when was it released?
It was published in July 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.
Pluribus— what is it used for?
It works in the domain of Games, and is recorded as handling the task of poker. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Pluribus— how much compute was used to train it?
Training consumed around 6.6 × 10¹⁶ 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.
Pluribus— 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.
Pluribus— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Pluribus— 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.
Pluribus— who created it?
It was published by Facebook AI Research, based in United States of America, an organisation 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.