Seq2Seq LSTM
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
- 10 September 2014
- Authors
- Ilya Sutskever, Oriol Vinyals, Quoc V. Le
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
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
- 1.9B
- Training data
- 870,000,000 tokens
- Epochs
- 7.5
The resulting LSTM has 384M parameters of which 64M are pure recurrent connections (32M for the “encoder” LSTM and 32M for the “decoder” LSTM). The paper uses an ensemble of 5 LSTMs.
[WORDS] "We used the WMT’14 English to French dataset. We trained our models on a subset of 12M sentences consisting of 348M French words and 304M English words, which is a clean “selected” subset from [29]."
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
- 5.6 × 10¹⁹ FLOP
- How it was established
- Operation counting,Hardware,Third-party estimation
384E+6 parameters * 2 FLOP/parameter * (348E+6 + 304E+6 points per epoch) * 7.5 epochs * 3 FLOP/point ~= 1.126656e+19 FLOP Times 5 independent models in ensemble => 5.6E+19 FLOP If we assume NVIDIA K40 (in use at the time): 10 days * 24 * 60 * 60 seconds/day * 8 GPUs * 33% * 5e12 FLOP/s * 5 models in ensemble ~= 5.7E+19 FLOP Authors of "AI and Memory Wall" estimated model's training compute as 11,000 PFLOPS = 1.1*10^19 FLOPS (https://github.com/amirgholami/ai_and_memory_wall)
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
- 240 hours (10 days)
Training took about 10 days
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
- Confident
- Citations
- 22,025
Sources
Where this record came from and when it was last checked.
- Reference
- Sequence to Sequence Learning with Neural Networks
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Seq2Seq LSTM was published by Google, in the country recorded as United States of America, during September 2014. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of translation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required arithmetic totalling around 5.6 × 10¹⁹ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 870,000,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
Seq2Seq LSTM — common questions
Seq2Seq LSTM— 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.
Seq2Seq LSTM— how many parameters does it have?
It has a parameter count of 1.9B. The resulting LSTM has 384M parameters of which 64M are pure recurrent connections (32M for the “encoder” LSTM and 32M for the “decoder” LSTM). The paper uses an ensemble of 5 LSTMs. 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.
Seq2Seq LSTM— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
Seq2Seq LSTM— when was it released?
It was published in September 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Seq2Seq LSTM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Seq2Seq LSTM— how much compute was used to train it?
Training consumed around 5.6 × 10¹⁹ 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.
Seq2Seq LSTM— 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.