Vine copula (wine quality)
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
- Massachusetts Institute of Technology (MIT),Rey Juan Carlos University
- Organisation type
- Academia,Academia
- Country
- United States of America, Spain
- Published
- 4 December 2018
- Authors
- Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
What it does
The problem areas the model was built for. A model can carry several of each.
- Task
- Multi-class classification
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.
- Training data
- tokens
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
- 8.5 × 10¹⁵ FLOP
- How it was established
- Hardware
"for a 6-dimensional data set of size 500, the RL algorithm finishes in approximately 15 minutes with a single GPU"" Assuming they used V100, fp16 and trained for similar amount of time: 31330000000000 FLOP / GPU / sec * 15 min * 60 sec / min * 0.3 [assumed utilization] = 8.4591e+15 FLOP
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
- 0 hours
" for a 6-dimensional data set of size 500, the RL algorithm finishes in approximately 15 minutes with a single GPU"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Vine Copula Models For Synthetic Data Generation
- Last updated
- 11 February 2026
What the numbers mean
What this model is
Vine copula (wine quality) was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, in United States of America, in December 2018. The organisation is categorised as academia,Academia.
and is recorded as doing multi-class classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 8.5 × 10¹⁵ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Answers
Vine copula (wine quality) — common questions
What GPU do I need to run Vine copula (wine quality)?
None. Vine copula (wine quality) 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 Vine copula (wine quality) open source?
The licensing for Vine copula (wine quality) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Vine copula (wine quality) have?
No parameter count has been published for Vine copula (wine quality), which is why no memory or speed figure appears on this page.
Who created Vine copula (wine quality)?
Vine copula (wine quality) was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, based in United States of America, categorised as academia,Academia.
When was Vine copula (wine quality) released?
Vine copula (wine quality) was published in December 2018. 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 Vine copula (wine quality) used for?
and is recorded as handling multi-class classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train Vine copula (wine quality)?
Around 8.5 × 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.