DreamerV3
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 Toronto
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, Canada
- Published
- 10 January 2023
- Authors
- Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Games
- Task
- Open ended play
- Numerical format
- BF16
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
- 200M
- Training data
- 1,600,000,000 tokens
Table B1
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
- 2.2 × 10²⁰ FLOP
- How it was established
- Hardware
16 environment instances, each with 1 V100 running for 17 days (table A1) - it's not entirely clear if the GPU days already account for multiple environment instances. Assuming no: Compute: 17*24*60*60*125000000000000*0.3=5.508e+19 Assuming yes: Compute: 17*24*60*60*16*125000000000000*0.3=8.8128e+20 Geometric mean: 220320000000000000000
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
- Chips used
- 16
- Wall-clock time
- 6,528 hours (272 days)
- Power draw
- 9.6 kW
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
- Open source
Apache 2.0 https://github.com/danijar/dreamerv3
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
- 1,073
Using the same hyperparameters across all domains, DreamerV3 outperforms specialized model-free and model-based algorithms in a wide range of benchmarks and data-efficiency regimes. Applied out of the box, DreamerV3 also learns to obtain diamonds in the popular video game Minecraft from scratch given sparse rewards, a long-standing challenge in artificial intelligence for which previous approaches required human data or domain-specific heuristics.
Sources
Where this record came from and when it was last checked.
- Reference
- Mastering Diverse Domains through World Models
- Last updated
- 25 May 2026
What the numbers mean
Background
DreamerV3 was published by DeepMind,University of Toronto, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during January 2023. The category the publisher falls under is industry,Academia.
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.
Training and provenance
Producing it required arithmetic totalling around 2.2 × 10²⁰ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 1,600,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
DreamerV3 — common questions
DreamerV3— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DreamerV3— how many parameters does it have?
It has a parameter count of 200M. Table B1. 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.
DreamerV3— who created it?
It was published by DeepMind,University of Toronto, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry,Academia.
DreamerV3— when was it released?
It was published in January 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DreamerV3— what is it used for?
It works in the domain of Games, and is recorded as handling the task of open ended play. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DreamerV3— how much compute was used to train it?
Training consumed around 2.2 × 10²⁰ FLOP, on hardware recorded as 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.
DreamerV3— 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.