LSTM-3-layer+Gadam
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
- University of Oxford,University of Bristol,University of Cambridge
- Organisation type
- Academia,Academia,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 2 March 2020
- Authors
- Diego Granziol, Xingchen Wan, Samuel Albanie, Stephen Roberts
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
- 24M
- Training data
- 912,344 tokens
- Epochs
- 200
24M imputed from Merity et al. 2017 https://arxiv.org/abs/1708.02182
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
- 2.6 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 24000000 parameters * 912344 tokens * 200 epochs = 2.6275507e+16 FLOP
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 GeForce RTX 2080 Ti 11GB
- Chips used
- 1
- Power draw
- 281 W
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 5
- Benchmark data
- LSTM-3-layer+Gadam
Sources
Where this record came from and when it was last checked.
- Reference
- Iterative Averaging in the Quest for Best Test Error
- Last updated
- 28 November 2025
What the numbers mean
Background
LSTM-3-layer+Gadam was published by University of Oxford,University of Bristol,University of Cambridge, in United Kingdom of Great Britain and Northern Ireland, in March 2020. It comes out of academia,Academia,Academia.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required around 2.6 × 10¹⁶ FLOP of arithmetic, on NVIDIA GeForce RTX 2080 Ti 11GB, which is a statement about the training budget rather than about inference.
Around 912,344 tokens went into training it.
Answers
LSTM-3-layer+Gadam — common questions
How much compute was used to train LSTM-3-layer+Gadam?
Around 2.6 × 10¹⁶ FLOP, on NVIDIA GeForce RTX 2080 Ti 11GB. 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.
What GPU do I need to run LSTM-3-layer+Gadam?
None. LSTM-3-layer+Gadam 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.
Is LSTM-3-layer+Gadam open source?
No. LSTM-3-layer+Gadam has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LSTM-3-layer+Gadam have?
LSTM-3-layer+Gadam has 24M parameters. 24M imputed from Merity et al. 2017 https://arxiv.org/abs/1708.02182. 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.
Who created LSTM-3-layer+Gadam?
LSTM-3-layer+Gadam was published by University of Oxford,University of Bristol,University of Cambridge, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia,Academia,Academia.
When was LSTM-3-layer+Gadam released?
LSTM-3-layer+Gadam was published in March 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is LSTM-3-layer+Gadam used for?
LSTM-3-layer+Gadam works in Language, and is recorded as handling 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?
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