Deep Boltzmann Machines
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
- University of Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 16 April 2009
- Authors
- Ruslan Salakhutdinov, Geoffrey Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification
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
- 60,000 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
- 2,666
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Boltzmann Machines
- Last updated
- 28 November 2025
What the numbers mean
About this model
Deep Boltzmann Machines was published by University of Toronto, in Canada, in April 2009. The organisation is categorised as academia.
It works in Other, and is recorded as doing image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on about 60,000 tokens of text.
Answers
Deep Boltzmann Machines — common questions
What GPU do I need to run Deep Boltzmann Machines?
None. Deep Boltzmann Machines 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 Deep Boltzmann Machines open source?
The licensing for Deep Boltzmann Machines 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 Deep Boltzmann Machines have?
No parameter count has been published for Deep Boltzmann Machines, which is why no memory or speed figure appears on this page.
Who created Deep Boltzmann Machines?
Deep Boltzmann Machines was published by University of Toronto, based in Canada, categorised as academia.
When was Deep Boltzmann Machines released?
Deep Boltzmann Machines was published in April 2009. 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 Deep Boltzmann Machines used for?
Deep Boltzmann Machines works in Other, and is recorded as handling image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
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.