Deep RNN (PTB)
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
- MetaMind Inc
- Organisation type
- Industry
- Country
- United States of America
- Published
- 11 December 2013
- Authors
- Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher
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.
- Parameters
- 6M
- Training data
- 929,000 tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 4,010
- Benchmark data
- Deep RNN
Sources
Where this record came from and when it was last checked.
- Reference
- Pointer Sentinel Mixture Models
- Last updated
- 25 May 2026
What the numbers mean
About this model
Deep RNN (PTB) was published by MetaMind Inc, in United States of America, in December 2013. It comes out of industry.
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.
Training and provenance
The training set ran to roughly 929,000 tokens.
Answers
Deep RNN (PTB) — common questions
What is Deep RNN (PTB) used for?
Deep RNN (PTB) works in Language, and is recorded as handling language modeling. 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.
What GPU do I need to run Deep RNN (PTB)?
None. Deep RNN (PTB) 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 Deep RNN (PTB) open source?
No. Deep RNN (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Deep RNN (PTB) have?
Deep RNN (PTB) has 6M parameters. 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.
Who created Deep RNN (PTB)?
Deep RNN (PTB) was published by MetaMind Inc, based in United States of America, categorised as industry.
When was Deep RNN (PTB) released?
Deep RNN (PTB) was published in December 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.