Vega v2
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
- Wuhan University,JD Explore Academy,Shanghai AI Lab,Nanyang Technological University,Washington University in St Louis,Chongqing University of Posts and Telecommunications,University of Sydney
- Organisation type
- Academia,Academia,Academia,Academia,Academia,Academia
- Country
- China, Singapore, United States of America, Australia
- Published
- 4 December 2022
- Authors
- Qihuang Zhong, Liang Ding, Yibing Zhan, Yu Qiao, Yonggang Wen, Li Shen, Juhua Liu, Baosheng Yu, Bo Du, Yixin Chen, Xinbo Gao, Chunyan Miao, Xiaoou Tang, Dacheng Tao
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Question answering, Word sense disambiguation
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
- 6B
- Training data
- 6,439,999,999 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
- 7.8 × 10²² FLOP
Pretraining took 1 month on 320 A100 (Section 3.1) 720*60*60*320*312000000000000*0.3=7.7635584e+22
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 A100
- Chips used
- 320
- Wall-clock time
- 720 hours (30 days)
- Power draw
- 255.9 kW
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 42
Highest score at SuperGLUE leaderboard version 2.0 in terms of RTE (Recognizing Textual Entailment-Accuracy)
Sources
Where this record came from and when it was last checked.
- Reference
- Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
- Last updated
- 25 May 2026
What the numbers mean
Background
Vega v2 was published by Wuhan University,JD Explore Academy,Shanghai AI Lab,Nanyang Technological University,Washington University in St Louis,Chongqing University of Posts and Telecommunications,University of Sydney, in China, in December 2022. academia,Academia,Academia,Academia,Academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling, Question answering, Word sense disambiguation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Producing it required around 7.8 × 10²² FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
The training set ran to roughly 6,439,999,999 tokens.
Its inclusion criterion is sOTA improvement.
Answers
Vega v2 — common questions
Is Vega v2 open source?
No. Vega v2 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Vega v2 have?
Vega v2 has 6B parameters. 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 Vega v2?
Vega v2 was published by Wuhan University,JD Explore Academy,Shanghai AI Lab,Nanyang Technological University,Washington University in St Louis,Chongqing University of Posts and Telecommunications,University of Sydney, based in China, categorised as academia,Academia,Academia,Academia,Academia,Academia.
When was Vega v2 released?
Vega v2 was published in December 2022. 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 Vega v2 used for?
Vega v2 works in Language, and is recorded as handling language modeling, Question answering, Word sense disambiguation. 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 Vega v2?
Around 7.8 × 10²² FLOP, on NVIDIA A100. 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 Vega v2?
None. Vega v2 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.