RNN LM
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
- Johns Hopkins University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 26 September 2010
- Authors
- Tomas Mikolov, M. Karafiát, L. Burget, J. Černocký, 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.
- Parameters
- 70.3M
- Training data
- 6,400,000 tokens
- Epochs
- 1
This database entry refers to the 3xRNN rows in Table 2 (static and dynamic likely use the same model ensemble, but allow the model weights to update once when testing the dynamic version). I assume the 3xRNN represents interpolation between the three largest models shown explicitly (RNN 250/5, RNN 250/2, and RNN 400/10). This seems likely, since smaller models do considerably worse on their own. In the following colab notebook, I estimate vocabulary sizes for the NYT Gigaword data at around 5…
As it is very time consuming to train RNN LM on large data, we have used only up to 6.4M words for training RNN models.
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
- 5.4 × 10¹⁶ FLOP
- How it was established
- Operation counting
"Convergence is usually achieved after 10-20 epochs." Training was done over a 6.4M subset of the NYT section of English Gigaword. 6 * 70,265,000 * 20 * 6.4M = 5.396e16
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
- 504 hours (21 days)
"it takes several weeks to train the most complex models." Rough guess, 3 weeks = 504 hours Assuming these models trained at the same time on different machines.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Speculative
- Citations
- 6,038
Sources
Where this record came from and when it was last checked.
- Reference
- Recurrent neural network based language model
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
RNN LM was published by Johns Hopkins University, in the country recorded as United States of America, during September 2010. The category the publisher falls under is academia.
It works in the domain of Language, and is recorded as performing the task of 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
Producing it required arithmetic totalling around 5.4 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 6,400,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
RNN LM — common questions
RNN LM— how much compute was used to train it?
Training consumed around 5.4 × 10¹⁶ 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.
RNN LM— 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 LM— 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 LM— how many parameters does it have?
It has a parameter count of 70.3M. This database entry refers to the 3xRNN rows in Table 2 (static and dynamic likely use the same model ensemble, but allow the model weights to update once when testing the dynamic version). I assume the 3xRNN represents interpolation between the three largest models shown explicitly (RNN 250/5, RNN 250/2, and RNN 400/10). This seems likely, since smaller models do considerably worse on their own. In the following colab notebook, I estimate vocabulary sizes for the NYT Gigaword data at around 54.4k, 41.4k, and 27.6k for merge thresholds of 2, 5, and 10, respectively: https://colab.research.google.com/drive/1K5qH0EqXtFwTLESNtp4oelCM28GpGXt6#scrollTo=tedUkbgklNJ3 So the total number of parameters in each constituent model is: - RNN 250/2: (250 + 54.4k) * 250 + (250 * 54.4k) = 27,262,500 - RNN 250/5: (250 + 41.4k) * 250 + (250 * 41.4k) = 20,762,500 - RNN 400/10: (400 + 27.6k) * 400 + (400 * 27.6k) = 22,240,000 In total: 70,265,000 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.
RNN LM— who created it?
It was published by Johns Hopkins University, based in United States of America, an organisation categorised as academia.
RNN LM— when was it released?
It was published in September 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RNN LM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.