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 United States of America, in October 2016. It comes out of industry.
It works in Language, and is recorded as doing 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
When was Differentiable neural computer released?
Differentiable neural computer 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.
What is Differentiable neural computer used for?
Differentiable neural computer works in Language, and is recorded as handling question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Differentiable neural computer?
None. Differentiable neural computer 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 Differentiable neural computer open source?
The licensing for Differentiable neural computer was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Differentiable neural computer have?
No parameter count has been published for Differentiable neural computer, which is why no memory or speed figure appears on this page.
Who created Differentiable neural computer?
Differentiable neural computer was published by Google DeepMind, based in United States of America, 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.