GPU DBNs
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 15 June 2009
- Authors
- R Raina, A Madhavan, AY Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Miscellaneous image analysis
- Numerical format
- FP32
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
- 100M
- Training data
- 121,344,000,000 tokens
"For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day."
Table 2 shows the running time for processing 1 million examples for RBMs of varying size
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1 × 10¹⁵ FLOP
- How it was established
- Hardware
https://www.getguesstimate.com/models/19602 6435 GPU seconds for 1M examples Single GTX 280 with 622.1 GFLOPS All results are reported for 1M examples, unclear if they ran larger training experiments.
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,Historical significance
- Record confidence
- Confident
- Citations
- 1,032
Sources
Where this record came from and when it was last checked.
- Reference
- Large-scale Deep Unsupervised Learning using Graphics Processors
- Last updated
- 28 November 2025
What the numbers mean
About this model
GPU DBNs was published by Stanford University, in the country recorded as United States of America, during June 2009. The category the publisher falls under is academia.
It works in the domain of Other, and is recorded as performing the task of miscellaneous image analysis.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took a computation budget of roughly 1 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 121,344,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited,Historical significance.
Answers
GPU DBNs — common questions
GPU DBNs— what is it used for?
It works in the domain of Other, and is recorded as handling the task of miscellaneous image analysis. These are the areas it was designed around; they describe intent rather than a hard boundary.
GPU DBNs— how much compute was used to train it?
Training consumed around 1 × 10¹⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
GPU DBNs— 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.
GPU DBNs— 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.
GPU DBNs— how many parameters does it have?
It has a parameter count of 100M. "For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day.". 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.
GPU DBNs— who created it?
It was published by Stanford University, based in United States of America, an organisation categorised as academia.
GPU DBNs— when was it released?
It was published in June 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.
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.