LSTM 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
- RWTH Aachen University
- Organisation type
- Academia
- Country
- Germany
- Published
- 9 September 2012
- Authors
- M. Sundermeyer, R. Schlüter, H. Ney
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
- 102.7M
- Training data
- 27,000,000 tokens
Multiple models were trained, the largest on transcribed French podcast data. "We trained an LSTM LM using 300 hidden nodes and 27 M running words of indomain training data" "Corpus sizes in number of running words; the vocabulary size of the Treebank corpus is 10 K, for Quaero French it is 170 K" Embedding and unembedding: 2*170000*300=102000000 LSTM: 4*600*300=720000 Total: 102000000+720000=102720000=1.03e8 (Assuming the embedding dimension is the same as the LSTM layer)
"We trained an LSTM LM using 300 hidden nodes and 27 M running words of indomain training data."
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
- 1.7 × 10¹⁶ FLOP
- How it was established
- Operation counting
FLOP per input for LSTM layer is 4*2*(M+N)*M, for N inputs and M outputs. Embedding FLOPs: 2 * 170000 * 300 = 102,000,000 LSTM FLOPs: 4 * 2 * (300 + 300) * 300 = 1,440,000 Unembedding FLOPs: 2 * 170000 * 300 = 102,000,000 Total: 205,440,000 FLOPs per word per forward pass For 27M training input words and including backward passes: 27M * 3 * 205,440,000 = 1.66e16 However, it sounds like they're doing something with a secondary acoustic model, so this may be an underestimate.
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
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- LSTM Neural Networks for Language Modeling
- Last updated
- 28 November 2025
What the numbers mean
About this model
LSTM LM was published by RWTH Aachen University, in the country recorded as Germany, during September 2012. The publishing organisation is categorised as 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 1.7 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 27,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
LSTM LM — common questions
LSTM 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.
LSTM LM— how many parameters does it have?
It has a parameter count of 102.7M. Multiple models were trained, the largest on transcribed French podcast data. "We trained an LSTM LM using 300 hidden nodes and 27 M running words of indomain training data" "Corpus sizes in number of running words; the vocabulary size of the Treebank corpus is 10 K, for Quaero French it is 170 K" Embedding and unembedding: 2*170000*300=102000000 LSTM: 4*600*300=720000 Total: 102000000+720000=102720000=1.03e8 (Assuming the embedding dimension is the same as the LSTM layer). 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.
LSTM LM— who created it?
It was published by RWTH Aachen University, based in Germany, an organisation categorised as academia.
LSTM LM— when was it released?
It was published in September 2012. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
LSTM 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.
LSTM LM— how much compute was used to train it?
Training consumed around 1.7 × 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.
LSTM 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.
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.