Hopfield network
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
- California Institute of Technology
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 April 1982
- Authors
- JJ Hopfield
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Sequence memorization
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
- 9.9K
- Training data
- tokens
My understanding is that the biggest Hopfield networks they studied had N=100 units. Each unit has 99 synapses Tij from each other unit, for a total of 100*99 parameters
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
- Highly cited
- Citations
- 23,315
Sources
Where this record came from and when it was last checked.
- Reference
- Neural networks and physical systems with emergent collective computational abilities
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Hopfield network was published by California Institute of Technology, in the country recorded as United States of America, during April 1982. It comes out of an organisation categorised as academia.
It works in the domain of Other, and is recorded as performing the task of sequence memorization.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Hopfield network — common questions
Hopfield network— how many parameters does it have?
It has a parameter count of 9.9K. My understanding is that the biggest Hopfield networks they studied had N=100 units. Each unit has 99 synapses Tij from each other unit, for a total of 100*99 parameters. 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.
Hopfield network— who created it?
It was published by California Institute of Technology, based in United States of America, an organisation categorised as academia.
Hopfield network— when was it released?
It was published in April 1982. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Hopfield network— what is it used for?
It works in the domain of Other, and is recorded as handling the task of sequence memorization. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Hopfield network— 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.
Hopfield network— 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.
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.