RNN-SpeedUp
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
- Brno University of Technology,Johns Hopkins University
- Organisation type
- Academia,Academia
- Country
- Czechia, United States of America
- Published
- 22 May 2011
- Authors
- T. Mikolov, S. Kombrink, L. Burget, J. Cernock ˇ y, and S. Khudanpur
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 929,000 tokens
Section 3: "The data used in the following experiments were obtained from Penn Tree Bank: sections 0-20 were used as training data (about 930K tokens)" 0.75 words per token for English
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,576
Sources
Where this record came from and when it was last checked.
- Reference
- Extensions of recurrent neural network language model
- Last updated
- 28 November 2025
What the numbers mean
About this model
RNN-SpeedUp was published by Brno University of Technology,Johns Hopkins University, in Czechia, in May 2011. The organisation is categorised as academia,Academia.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Around 929,000 tokens went into training it.
Answers
RNN-SpeedUp — common questions
What GPU do I need to run RNN-SpeedUp?
None. RNN-SpeedUp 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 RNN-SpeedUp open source?
The licensing for RNN-SpeedUp 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 RNN-SpeedUp have?
No parameter count has been published for RNN-SpeedUp, which is why no memory or speed figure appears on this page.
Who created RNN-SpeedUp?
RNN-SpeedUp was published by Brno University of Technology,Johns Hopkins University, based in Czechia, categorised as academia,Academia.
When was RNN-SpeedUp released?
RNN-SpeedUp was published in May 2011. 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 RNN-SpeedUp used for?
RNN-SpeedUp works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.