GloVe (6B)
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 January 2014
- Authors
- J Pennington, R Socher, CD Manning
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Semantic embedding
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
- 120M
- Training data
- 66,453,980 tokens
400k vocab * 300 vector dimensions
"We trained our model on five corpora of varying sizes: a 2010 Wikipedia dump with 1 billion tokens; a 2014 Wikipedia dump with 1.6 billion tokens; Gigaword 5 which has 4.3 billion tokens; the combination Gigaword5 + Wikipedia2014, which has 6 billion tokens; and on 42 billion tokens of web data, from Common Crawl [To demonstrate the scalability of the model, we also trained it on a much larger sixth corpus, containing 840 billion tokens of web data, but in this case we did not lowercase the vo…
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Citations
- 30,643
Sources
Where this record came from and when it was last checked.
- Reference
- GloVe: Global Vectors for Word Representation
- Last updated
- 11 February 2026
What the numbers mean
Background
GloVe (6B) was published by Stanford University, in United States of America, in January 2014. The organisation is categorised as academia.
It works in Language, and is recorded as doing semantic embedding.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Around 66,453,980 tokens went into training it.
Its inclusion criterion is highly cited.
Answers
GloVe (6B) — common questions
How many parameters does GloVe (6B) have?
GloVe (6B) has 120M parameters. 400k vocab * 300 vector dimensions. 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 GloVe (6B)?
GloVe (6B) was published by Stanford University, based in United States of America, categorised as academia.
When was GloVe (6B) released?
GloVe (6B) was published in January 2014. 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 GloVe (6B) used for?
GloVe (6B) works in Language, and is recorded as handling semantic embedding. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run GloVe (6B)?
None. GloVe (6B) 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 GloVe (6B) open source?
The licensing for GloVe (6B) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.