Differentiable neural computer
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 October 2016
- Authors
- Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu & Demis Hassabis
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering
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
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,668
Sources
Where this record came from and when it was last checked.
- Reference
- Hybrid computing using a neural network with dynamic external memory
- Last updated
- 1 January 2026
What the numbers mean
Background
Differentiable neural computer was published by Google DeepMind, in the country recorded as United States of America, during October 2016. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of question answering.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Differentiable neural computer — common questions
Differentiable neural computer— when was it released?
It was published in October 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Differentiable neural computer— what is it used for?
It works in the domain of Language, and is recorded as handling the task of question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Differentiable neural computer— 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.
Differentiable neural computer— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Differentiable neural computer— 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.
Differentiable neural computer— who created it?
It was published by Google DeepMind, based in United States of America, 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.