OpenAI Five Rerun
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
- OpenAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 13 December 2019
- Authors
- Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław “Psyho" Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Su…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Dota 2
- Approach
- Self-supervised learning
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
- 159M
- Training data
- 53,084,160,000 tokens
"We define a policy (π) as a function from the history of observations to a probability distribution over actions, which we parameterize as a recurrent neural network with approximately 159 million parameters (θ)." pg. 3 of paper source: https://docs.google.com/spreadsheets/d/1Kj4Q5WADcDXtUJLIOfGTCE3tGvxNczEMwyy8QtgSkHk/edit#gid=54587040&fvid=1361937389
54k iterations (Fig 7) with a batch size of 983040 (Table 2)
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
- 1.3 × 10²² FLOP
- How it was established
- Third-party estimation
THIS CALCULATION IS FOR RERUN "Rerun took 2 months and 150 ± 5 PFlops/s·days of compute (see Figure 4)" source: https://docs.google.com/spreadsheets/d/1Kj4Q5WADcDXtUJLIOfGTCE3tGvxNczEMwyy8QtgSkHk/edit#gid=54587040&fvid=1361937389
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 P100
- Chips used
- 512
- Power draw
- 262.1 kW
- Compute cost
- $321,106
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,SOTA improvement
- Record confidence
- Confident
- Citations
- 2,137
"On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game."
Sources
Where this record came from and when it was last checked.
- Reference
- Dota 2 with Large Scale Deep Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
What this model is
OpenAI Five Rerun was published by OpenAI, in the country recorded as United States of America, during December 2019. The publishing organisation is categorised as industry.
It works in the domain of Games, and is recorded as performing the task of dota 2.
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 1.3 × 10²² FLOP, on hardware recorded as NVIDIA P100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 53,084,160,000 tokens of text.
Its inclusion criterion: highly cited,SOTA improvement.
Answers
OpenAI Five Rerun — common questions
OpenAI Five Rerun— when was it released?
It was published in December 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.
OpenAI Five Rerun— what is it used for?
It works in the domain of Games, and is recorded as handling the task of dota 2. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
OpenAI Five Rerun— how much compute was used to train it?
Training consumed around 1.3 × 10²² FLOP, on hardware recorded as NVIDIA P100. 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.
OpenAI Five Rerun— 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.
OpenAI Five Rerun— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
OpenAI Five Rerun— how many parameters does it have?
It has a parameter count of 159M. "We define a policy (π) as a function from the history of observations to a probability distribution over actions, which we parameterize as a recurrent neural network with approximately 159 million parameters (θ)." pg. 3 of paper 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.
OpenAI Five Rerun— who created it?
It was published by OpenAI, 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.