BIG LSTM+CNN INPUTS
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
- Google Brain
- Organisation type
- Industry
- Country
- United States of America
- Published
- 11 February 2016
- Authors
- Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu
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
- 1B
- Training data
- tokens
1.04B (Table 1)
"The experiments are performed on the 1B Word Benchmark data set introduced by (Chelba et al., 2013), which is a publicly available benchmark for measuring progress of statistical language modeling. The data set contains about 0.8B words with a vocabulary of 793471 words, including sentence boundary markers."
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 × 10²⁰ FLOP
- How it was established
- Hardware,Operation counting
assuming (!) 10 days similarly to another model in the paper 240*3600*5046000000000*32*0.3 = 4.1853542e+19 assuming (!) 50 epochs similarly to another model in the paper 6*1.04*10^9*0.8*10^9*50 = 2.496e+20 sqrt(4.1853542e+19*2.496e+20) = 1.0220883e+20
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla K40s
- Chips used
- 32
- Chip-hours
- 240
- Power draw
- 16.6 kW
- Compute cost
- $71
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Exploring the Limits of Language Modeling
- Last updated
- 28 November 2025
What the numbers mean
What this model is
BIG LSTM+CNN INPUTS was published by Google Brain, in the country recorded as United States of America, during February 2016. It comes out of an organisation 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.
How it was trained
Producing it required arithmetic totalling around 1 × 10²⁰ FLOP, on hardware recorded as NVIDIA Tesla K40s. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
BIG LSTM+CNN INPUTS — common questions
BIG LSTM+CNN INPUTS— 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.
BIG LSTM+CNN INPUTS— 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.
BIG LSTM+CNN INPUTS— how many parameters does it have?
It has a parameter count of 1B. 1.04B (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.
BIG LSTM+CNN INPUTS— who created it?
It was published by Google Brain, based in United States of America, an organisation categorised as industry.
BIG LSTM+CNN INPUTS— when was it released?
It was published in February 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
BIG LSTM+CNN INPUTS— 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.
BIG LSTM+CNN INPUTS— how much compute was used to train it?
Training consumed around 1 × 10²⁰ FLOP, on hardware recorded as NVIDIA Tesla K40s. 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.
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Looking at it from the other side?
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