DLWP
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
- University of Washington,Microsoft Research
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 9 February 2021
- Authors
- Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay
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
- 2.7M
- Training data
- tokens
Table 1
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
- 5.7 × 10¹⁸ FLOP
- How it was established
- Hardware
31330000000000 FLOP / GPU/ sec [Tesla V100] * 1 GPU * 168 hours * 3600 sec / hour * 0.3 [assumed utilization] = 5.6845152e+18 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 Tesla V100 DGXS 32 GB
- Chips used
- 1
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 279 W
"Our DLWP model requires 6–8 days of computation to train on a single Tesla V100 GPU 7 days = 168 hours
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
Sources
Where this record came from and when it was last checked.
- Reference
- Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
- Last updated
- 28 November 2025
What the numbers mean
About this model
DLWP was published by University of Washington,Microsoft Research, in United States of America, in February 2021. It comes out of academia,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 5.7 × 10¹⁸ FLOP of arithmetic, on NVIDIA Tesla V100 DGXS 32 GB, which is a statement about the training budget rather than about inference.
Its inclusion criterion is sOTA improvement.
Answers
DLWP — common questions
How many parameters does DLWP have?
DLWP has 2.7M parameters. Table 1. 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 DLWP?
DLWP was published by University of Washington,Microsoft Research, based in United States of America, categorised as academia,Industry.
When was DLWP released?
DLWP was published in February 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.
What is DLWP used for?
DLWP works in Earth science, and is recorded as handling weather forecasting. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train DLWP?
Around 5.7 × 10¹⁸ FLOP, on NVIDIA Tesla V100 DGXS 32 GB. 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 DLWP?
None. DLWP 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.
Is DLWP open source?
No. DLWP has not had its weights published, so it exists only as a service controlled by its owner.
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.