Aurora
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
- Microsoft Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 28 May 2024
- Authors
- Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Tambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Earth science
- Task
- Weather forecasting
- 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
- 1.3B
- Training data
- tokens
700B tokens (see Figure 4) Total 1,219.91 TB 11,023,730 frames "All models are pretrained for 150 k steps on 32 GPUs, with a batch size of one per GPU."
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
- 4.5 × 10²¹ FLOP
- How it was established
- Hardware
"The model is trained using bf16 mixed precision." 312000000000000*420*32*3600*.3 = 4528742400000000000000 FLOP
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 A100
- Chips used
- 32
- Wall-clock time
- 420 hours (17.5 days)
- Power draw
- 25.3 kW
"32 A100 GPUs, which corresponds to approximately two and a half weeks of training" 17,5 days * 24 hours = 420
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.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Aurora: A Foundation Model of the Atmosphere
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Aurora was published by Microsoft Research, in the country recorded as United States of America, during May 2024. The publishing organisation is categorised as industry.
It works in the domain of Earth science, and is recorded as performing the task of weather forecasting.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took a computation budget of roughly 4.5 × 10²¹ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Aurora — common questions
Aurora— how much compute was used to train it?
Training consumed around 4.5 × 10²¹ FLOP, on hardware recorded as NVIDIA A100. 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.
Aurora— 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.
Aurora— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Aurora— how many parameters does it have?
It has a parameter count of 1.3B. 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.
Aurora— who created it?
It was published by Microsoft Research, based in United States of America, an organisation categorised as industry.
Aurora— when was it released?
It was published in May 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Aurora— what is it used for?
It works in the domain of Earth science, and is recorded as handling the task of weather forecasting. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.