DistBelief Vision
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 3 December 2012
- Authors
- J. Dean, G. Corrado, R. Monga, Kai Chen, M. Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, A. Senior, P. Tucker, Ke Yang, A. Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- 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.
- Parameters
- 1.7B
- Training data
- 16,000,000 tokens
"we used Downpour SGD to train the 1.7 billion parameter image model"
For visual object recognition we trained a larger neural network with locally-connected receptive fields on the ImageNet data set of 16 million images
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,Highly cited
- Record confidence
- Likely
" We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet"
Sources
Where this record came from and when it was last checked.
- Reference
- Large Scale Distributed Deep Networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DistBelief Vision was published by Google, in the country recorded as United States of America, during December 2012. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on a corpus of about 16,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement,Highly cited.
Answers
DistBelief Vision — common questions
DistBelief Vision— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DistBelief Vision— 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.
DistBelief Vision— 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.
DistBelief Vision— how many parameters does it have?
It has a parameter count of 1.7B. "we used Downpour SGD to train the 1.7 billion parameter image model". 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.
DistBelief Vision— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
DistBelief Vision— when was it released?
It was published in December 2012. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.