GCRN-M1, 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
- Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 22 December 2016
- Authors
- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson
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
- 42M
- Training data
- 929,000 tokens
- Epochs
- 13
42M (Table 2)
"All experiments have 13 epochs"
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
- 3 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 42000000 parameters * 929000 tokens * 13 epochs = 3.043404e+15 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
- 957
- Benchmark data
- GCRN-M1, dropout
Sources
Where this record came from and when it was last checked.
- Reference
- Structured Sequence Modeling with Graph Convolutional Recurrent Networks
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
GCRN-M1, dropout was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL), in Switzerland, in December 2016. academia is the category the publisher falls under.
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.
What went into building it
The training run consumed about 3 × 10¹⁵ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 929,000 tokens.
Answers
GCRN-M1, dropout — common questions
What GPU do I need to run GCRN-M1, dropout?
None. GCRN-M1, 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.
Is GCRN-M1, dropout open source?
No. GCRN-M1, dropout has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GCRN-M1, dropout have?
GCRN-M1, dropout has 42M parameters. 42M (Table 2). 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 GCRN-M1, dropout?
GCRN-M1, dropout was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL), based in Switzerland, categorised as academia.
When was GCRN-M1, dropout released?
GCRN-M1, dropout was published in December 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.
What is GCRN-M1, dropout used for?
GCRN-M1, dropout works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train GCRN-M1, dropout?
Around 3 × 10¹⁵ 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.
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.