AWD-FWM (WT2)
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
- IDSIA,Microsoft Research
- Organisation type
- Academia,Industry
- Country
- Switzerland, United States of America
- Published
- 16 November 2020
- Authors
- Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 37M
- Training data
- 2,000,000 tokens
- Epochs
- 1,600
"all WT2 models have roughly 37M parameters"
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
- 7.1 × 10¹⁷ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 37000000 parameters * 2000000 tokens * 1600 epochs = 7.104e+17 FLOP
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
code, repo license is MIT: https://github.com/ischlag/Fast-Weight-Memory-public/tree/main/language-modelling/fwm train and eval code: https://github.com/ischlag/Fast-Weight-Memory-public/blob/main/language-modelling/fwm/FWM-README.md
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 64
- Benchmark data
- AWD-FWM (WT2)
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Associative Inference Using Fast Weight Memory
- Last updated
- 25 May 2026
What the numbers mean
Background
AWD-FWM (WT2) was published by IDSIA,Microsoft Research, in Switzerland, in November 2020. It comes out of academia,Industry.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 7.1 × 10¹⁷ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 2,000,000 tokens went into training it.
Answers
AWD-FWM (WT2) — common questions
How much compute was used to train AWD-FWM (WT2)?
Around 7.1 × 10¹⁷ FLOP. 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 AWD-FWM (WT2)?
None. AWD-FWM (WT2) 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 AWD-FWM (WT2) open source?
No. AWD-FWM (WT2) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AWD-FWM (WT2) have?
AWD-FWM (WT2) has 37M parameters. "all WT2 models have roughly 37M parameters". 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 AWD-FWM (WT2)?
AWD-FWM (WT2) was published by IDSIA,Microsoft Research, based in Switzerland, categorised as academia,Industry.
When was AWD-FWM (WT2) released?
AWD-FWM (WT2) was published in November 2020. 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 AWD-FWM (WT2) used for?
AWD-FWM (WT2) works in Language, and is recorded as handling language modeling. 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.