Mogrifier RLSTM (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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 3 November 2022
- Authors
- Gábor Melis
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
- 35M
- Training data
- 2,666,667 tokens
- Epochs
- 250
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
- 1.4 × 10¹⁷ FLOP
- How it was established
- Operation counting
6ND = 6*35000000*2666667*250 = 1.4000002e+17
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
- Benchmark data
- Mogrifier RLSTM (WT2)
"On top of these improvements, the RLSTM outperformed the LSTM by a small margin, and we established a new state of the art on both datasets"
Sources
Where this record came from and when it was last checked.
- Reference
- Circling Back to Recurrent Models of Language
- Last updated
- 11 February 2026
What the numbers mean
Background
Mogrifier RLSTM (WT2) was published by DeepMind, in United Kingdom of Great Britain and Northern Ireland, in November 2022. It comes out of industry.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took roughly 1.4 × 10¹⁷ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 2,666,667 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Mogrifier RLSTM (WT2) — common questions
What is Mogrifier RLSTM (WT2) used for?
Mogrifier RLSTM (WT2) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Mogrifier RLSTM (WT2)?
Around 1.4 × 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 Mogrifier RLSTM (WT2)?
None. Mogrifier RLSTM (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 Mogrifier RLSTM (WT2) open source?
No. Mogrifier RLSTM (WT2) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Mogrifier RLSTM (WT2) have?
Mogrifier RLSTM (WT2) has 35M 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 Mogrifier RLSTM (WT2)?
Mogrifier RLSTM (WT2) was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Mogrifier RLSTM (WT2) released?
Mogrifier RLSTM (WT2) was published in November 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.