FGN
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 June 2025
- Authors
- Ferran Alet, Ilan Price, Andrew El-Kadi, Dominic Masters, Stratis Markou, Tom R. Andersson, Jacklynn Stott, Remi Lam, Matthew Willson, Alvaro Sanchez-Gonzalez, Peter Battaglia
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Earth science
- Task
- Weather forecasting
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
- 720M
- Training data
- tokens
"In this work we train 𝐽 = 4 models. To ensemble their predictions, we generate an equal number of ensemble member trajectories from each model" "FGN is a larger model, with ∼180m parameters per model seed" They train a 4 model ensemble with 180M parameters per model. Total parameters: 4*180=720M
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
- 9.6 × 10²¹ FLOP
- How it was established
- Hardware
TPU v5 FLOPs: 459000000000000 TPU v6 FLOPs: 918000000000000 They train for 490 TPU days, with an unspecified mix of v5 and v6. Assuming same proportion of both TPU and a utilization of 0.33. Mean TPU FLOPs: 688500000000000 Compute: 490*24*60*60*688500000000000*0.33=9618950880000001000000
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 v5p,Google TPU v6e Trillium
- Wall-clock time
- 72 hours
"Training FGN takes approximately 3 wall clock days, using a combined total of 490 TPUv5p and TPUv6e days of compute. " It's unclear if training a single model or the whole ensemble takes 3 days.
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
- Confident
"This paper presents FGN, a simple, scalable and flexible modeling approach which significantly outperforms the current state-of-the-art models" "FGN achieves state-of-the-art cyclone track prediction" "Our results show FGN offers substantial improvements over previous ML-based probabilistic weather models, and sets a new state-of-the-art in ensemble forecasting. It outperforms GenCast with both the skill of its marginal forecasts, including on extreme weather, as well as its skill in forecast…
Sources
Where this record came from and when it was last checked.
- Reference
- Skillful joint probabilistic weather forecasting from marginals
- Last updated
- 28 November 2025
What the numbers mean
Background
FGN was published by Google DeepMind, in United States of America, in June 2025. The organisation is categorised as industry.
It works in Earth science, and is recorded as doing 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
Producing it required around 9.6 × 10²¹ FLOP of arithmetic, on Google TPU v5p,Google TPU v6e Trillium, which is a statement about the training budget rather than about inference.
Its inclusion criterion is sOTA improvement.
Answers
FGN — common questions
Is FGN open source?
No. FGN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does FGN have?
FGN has 720M parameters. "In this work we train 𝐽 = 4 models. To ensemble their predictions, we generate an equal number of ensemble member trajectories from each model" "FGN is a larger model, with ∼180m parameters per model seed" They train a 4 model ensemble with 180M parameters per model. Total parameters: 4*180=720M. 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.
Who created FGN?
FGN was published by Google DeepMind, based in United States of America, categorised as industry.
When was FGN released?
FGN was published in June 2025.
What is FGN used for?
FGN works in Earth science, and is recorded as handling weather forecasting. 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 FGN?
Around 9.6 × 10²¹ FLOP, on Google TPU v5p,Google TPU v6e Trillium. 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 FGN?
None. FGN 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.