RNN for 1B words
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
- 11 December 2013
- Authors
- Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson
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
- 20B
- Training data
- 1,000,000,000 tokens
20B from Table 1
from abstract: 'With almost one billion words of training data, '
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 24
- Chip-hours
- 5,760
- Wall-clock time
- 240 hours (10 days)
from Table 1,240 hours on 24 CPUs
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,SOTA improvement
- Record confidence
- Speculative
- Citations
- 1,205
from abstract: 'We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned Kneser-Ney 5-gram model achieves perplexity 67.6; a combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. '
Sources
Where this record came from and when it was last checked.
- Reference
- One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
- Last updated
- 28 November 2025
What the numbers mean
What this model is
RNN for 1B words was published by Google, in the country recorded as United States of America, during December 2013. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training consumed a corpus of around 1,000,000,000 tokens of text.
Its inclusion criterion: highly cited,SOTA improvement.
Answers
RNN for 1B words — common questions
RNN for 1B words— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.
RNN for 1B words— 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.
RNN for 1B words— 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.
RNN for 1B words— how many parameters does it have?
It has a parameter count of 20B. 20B from 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.
RNN for 1B words— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
RNN for 1B words— when was it released?
It 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.