Relational Memory Core
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,University College London (UCL)
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 5 June 2018
- Authors
- Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap
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.
- Training data
- 4,000,000,000 tokens
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
looks like code for the architecture, but not experiment code: https://github.com/google-deepmind/sonnet/blob/v1/sonnet/python/modules/relational_memory.py
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
- Citations
- 235
- Benchmark data
- Relational Memory Core
"Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets."
Sources
Where this record came from and when it was last checked.
- Reference
- Relational recurrent neural networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Relational Memory Core was published by DeepMind,University College London (UCL), in the country recorded as United Kingdom of Great Britain and Northern Ireland, during June 2018. The category the publisher falls under is industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 4,000,000,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
Relational Memory Core — common questions
Relational Memory Core— 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.
Relational Memory Core— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Relational Memory Core— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Relational Memory Core— who created it?
It was published by DeepMind,University College London (UCL), based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry,Academia.
Relational Memory Core— when was it released?
It was published in June 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Relational Memory Core— 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.
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.