DistBelief Speech
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
- Speech
- Task
- Speech recognition (ASR)
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
- 47.2M
- Training data
- 1,100,000,000 tokens
"We used a deep network with five layers: four hidden layer with sigmoidal activations and 2560 nodes each, and a softmax output layer with 8192 nodes." "The network was fully-connected layer-to-layer, for a total of approximately 42 million model parameters." 2560*2560*4+2560*8192=47185920
"We trained on a data set of 1.1 billion weakly labeled examples"
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
- 3.1 × 10¹⁷ FLOP
- How it was established
- Operation counting
https://www.wolframalpha.com/input?i=6+FLOP+*+47185920+*+1.1+billion Number of epochs unknown but most likely 1 and probably under 30. We could narrow down the uncertainty further if we knew something about the hardware.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 120 hours
Figure 4
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
- Record confidence
- Speculative
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
Background
DistBelief Speech was published by Google, in the country recorded as United States of America, during December 2012. The publishing organisation is categorised as industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 3.1 × 10¹⁷ 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 1,100,000,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
DistBelief Speech — common questions
DistBelief Speech— 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 Speech— how many parameters does it have?
It has a parameter count of 47.2M. "We used a deep network with five layers: four hidden layer with sigmoidal activations and 2560 nodes each, and a softmax output layer with 8192 nodes." "The network was fully-connected layer-to-layer, for a total of approximately 42 million model parameters." 2560*2560*4+2560*8192=47185920. 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 Speech— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
DistBelief Speech— 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.
DistBelief Speech— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). 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.
DistBelief Speech— how much compute was used to train it?
Training consumed around 3.1 × 10¹⁷ 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.
DistBelief Speech— 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.
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.