GOAT
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 July 2021
- Authors
- Open-Ended Learning Team*, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard and Wojciech Marian Czarnecki
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Open ended play
- 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
- 3.5M
- Training data
- 798,720,000,000,000 tokens
- Batch size
- 64
estimate described here: https://docs.google.com/document/d/1S9xZyCeITDOs-P1W_-liNW0WgVN-OLsSudVrPXMaLqw/edit?usp=sharing
Figure 16 shows steps per generation and agent. In total there are 1.5e10 + 4.0e10 + 2.5e10 + 1.1e11 + 2e11 = 3.9e11 steps per agent.
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
- 2.4 × 10²² FLOP
- How it was established
- Hardware
[Final calculation] (8 TPUs) * (1.23e14 FLOP/TPU-s) * (0.1 utilization) / (50k steps/s) = 1.968e9 FLOP/step (32 agents) * (383B steps/agent) * (1.968e9 FLOP/step) = 2.412e22 FLOPs ========================== NOTES BELOW 6.1: Each agent is trained using 8 TPUv3s and consumes approximately 50,000 agent steps (observations) per second. Multiple agents interacting probably mean a fairly low utilization rate, so let’s assume 0.10 8 * 1.23e14 * 0.1 / 50k = 1.968e9 FLOPs per step The paper doesn’t s…
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
- Google TPU v3
- Compute cost
- $84,800
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.
- Foundation model
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 228
likely qualitatively SOTA I do not see any standard benchmarks that they are claiming SOTA on
Sources
Where this record came from and when it was last checked.
- Reference
- Open-Ended Learning Leads to Generally Capable Agents
- Last updated
- 25 May 2026
What the numbers mean
About this model
GOAT was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during July 2021. The publishing organisation is categorised as industry.
It works in the domain of Games, and is recorded as performing the task of open ended play.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 2.4 × 10²² FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 798,720,000,000,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
GOAT — common questions
GOAT— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GOAT— how many parameters does it have?
It has a parameter count of 3.5M. estimate described here: https://docs.google.com/document/d/1S9xZyCeITDOs-P1W_-liNW0WgVN-OLsSudVrPXMaLqw/edit?usp=sharing. 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.
GOAT— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
GOAT— when was it released?
It was published in July 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
GOAT— what is it used for?
It works in the domain of Games, and is recorded as handling the task of open ended play. 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.
GOAT— how much compute was used to train it?
Training consumed around 2.4 × 10²² FLOP, on hardware recorded as Google TPU v3. 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.
GOAT— 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.
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.