Mogrifier RLSTM (PTB)
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
- 24M
- Training data
- 1,238,667 tokens
- Epochs
- 400
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
- 7.1 × 10¹⁶ FLOP
- How it was established
- Operation counting
6ND = 6*24000000*1238667*400 = 7.1347219e+16
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
- Benchmark data
- Mogrifier RLSTM (PTB)
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
What this model is
Mogrifier RLSTM (PTB) was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during November 2022. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took a computation budget of roughly 7.1 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 1,238,667 tokens of text.
Answers
Mogrifier RLSTM (PTB) — common questions
Mogrifier RLSTM (PTB)— when was it released?
It 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.
Mogrifier RLSTM (PTB)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
Mogrifier RLSTM (PTB)— how much compute was used to train it?
Training consumed around 7.1 × 10¹⁶ 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.
Mogrifier RLSTM (PTB)— 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.
Mogrifier RLSTM (PTB)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Mogrifier RLSTM (PTB)— how many parameters does it have?
It has a parameter count of 24M. 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.
Mogrifier RLSTM (PTB)— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
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.