Libratus
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 19 August 2017
- Authors
- N Brown, T Sandholm, S Machine
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
- 5.5 × 10²⁰ FLOP
- How it was established
- Hardware
"In total, Libratus used about 25 million core hours. Of those, about 13 million core hours were used for exploratory experiments and evaluation. About 6 million core hours were spent on the initial abstraction and equilibrium finding component, another 3 million were used for nested subgame solving, and about 3 million were used on the self-improvement algorithm." "Like many data-centric supercomputers, Bridges offers a relatively a modest number of FLOPS, but lots of memory: 895 teraflops and…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chip-hours
- 857,000
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
- 104
Claims to be first ML system to reach superhuman level at No Limit Poker Texas Hold Em "Heads-up nolimit Texas Hold’em has long been the primary benchmark challenge for imperfect-information games. In January 2017 Libratus beat a team of four top-10 headsup no-limit specialist professionals in a 120,000-hand 20-day Brains vs. AI challenge match. That is the first time an AI has beaten top humans in this game. Libratus beat the humans by a large margin (147 mbb/hand), with 99.98% statistical sig…
Sources
Where this record came from and when it was last checked.
- Reference
- Libratus: The Superhuman AI for No-Limit Poker
- Last updated
- 1 January 2026
What the numbers mean
Background
Libratus was published by Carnegie Mellon University (CMU), in United States of America, in August 2017. academia is the category the publisher falls under.
It works in Games, and is recorded as doing poker.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required around 5.5 × 10²⁰ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Libratus — common questions
What is Libratus used for?
Libratus works in Games, and is recorded as handling 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.
How much compute was used to train Libratus?
Around 5.5 × 10²⁰ 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 Libratus?
None. Libratus 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 Libratus open source?
No. Libratus has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Libratus have?
No parameter count has been published for Libratus, which is why no memory or speed figure appears on this page.
Who created Libratus?
Libratus was published by Carnegie Mellon University (CMU), based in United States of America, categorised as academia.
When was Libratus released?
Libratus was published in August 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.
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.