Densely Connected LSTM + Var. Dropout
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
- Ghent University
- Organisation type
- Academia
- Country
- Belgium
- Published
- 19 July 2017
- Authors
- Fréderic Godin, Joni Dambre, Wesley De Neve
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
- 23M
- Training data
- 929,000 tokens
- Epochs
- 100
23M (Table 1)
"We trained for 100 epochs and used early stopping."
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
- 1.3 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 23000000 parameters * 929000 tokens * 100 epochs = 1.28202e+16 FLOP
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
- Confident
- Citations
- 7
- Benchmark data
- Densely Connected LSTM + Var. Dropout
Sources
Where this record came from and when it was last checked.
- Reference
- Improving Language Modeling using Densely Connected Recurrent Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
Background
Densely Connected LSTM + Var. Dropout was published by Ghent University, in Belgium, in July 2017. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 1.3 × 10¹⁶ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 929,000 tokens went into training it.
Answers
Densely Connected LSTM + Var. Dropout — common questions
Is Densely Connected LSTM + Var. Dropout open source?
No. Densely Connected LSTM + Var. Dropout has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Densely Connected LSTM + Var. Dropout have?
Densely Connected LSTM + Var. Dropout has 23M parameters. 23M (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.
Who created Densely Connected LSTM + Var. Dropout?
Densely Connected LSTM + Var. Dropout was published by Ghent University, based in Belgium, categorised as academia.
When was Densely Connected LSTM + Var. Dropout released?
Densely Connected LSTM + Var. Dropout was published in July 2017. 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 Densely Connected LSTM + Var. Dropout used for?
Densely Connected LSTM + Var. Dropout 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.
How much compute was used to train Densely Connected LSTM + Var. Dropout?
Around 1.3 × 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.
What GPU do I need to run Densely Connected LSTM + Var. Dropout?
None. Densely Connected LSTM + Var. Dropout 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.