PreTrans-3L-250H
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
- 22 March 2013
- Authors
- Alex Graves, Abdel-rahman Mohamed, Geoffrey Hinton
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
- 43M
- Training data
- tokens
Table 1
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
- 8,965
Sources
Where this record came from and when it was last checked.
- Reference
- Speech Recognition with Deep Recurrent Neural Networks
- Last updated
- 25 May 2026
What the numbers mean
About this model
PreTrans-3L-250H was published by University of Toronto, in the country recorded as Canada, during March 2013. It comes out of an organisation categorised as academia.
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 reason it appears in this catalogue at all: highly cited.
Answers
PreTrans-3L-250H — common questions
PreTrans-3L-250H— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
PreTrans-3L-250H— 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.
PreTrans-3L-250H— 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.
PreTrans-3L-250H— how many parameters does it have?
It has a parameter count of 43M. Table 1. 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.
PreTrans-3L-250H— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
PreTrans-3L-250H— when was it released?
It was published in March 2013. 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.