InternViT-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
- Shanghai AI Lab,Nanjing University,The University of Hong Kong,Tsinghua University,SenseTime,University of Science and Technology of China (USTC)
- Organisation type
- Academia,Academia,Academia,Academia,Industry,Academia
- Country
- China, Hong Kong
- Published
- 15 January 2024
- Authors
- Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, Jifeng Dai
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Language
- Task
- Visual question answering
- Base model
- LLaMA-7B
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
- tokens
6B
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
- 640
- Power draw
- 507.1 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
- Last updated
- 28 November 2025
What the numbers mean
About this model
InternViT-6B was published by Shanghai AI Lab,Nanjing University,The University of Hong Kong,Tsinghua University,SenseTime,University of Science and Technology of China (USTC), in China, in January 2024. The organisation is categorised as academia,Academia,Academia,Academia,Industry,Academia.
It works in Vision, Language, and is recorded as doing visual question answering.
It is derived from LLaMA-7B rather than trained from scratch, which is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
InternViT-6B — common questions
How many parameters does InternViT-6B have?
InternViT-6B has 6B parameters. 6B. 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 InternViT-6B?
InternViT-6B was published by Shanghai AI Lab,Nanjing University,The University of Hong Kong,Tsinghua University,SenseTime,University of Science and Technology of China (USTC), based in China, categorised as academia,Academia,Academia,Academia,Industry,Academia.
When was InternViT-6B released?
InternViT-6B was published in January 2024. 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 InternViT-6B used for?
InternViT-6B works in Vision, Language, and is recorded as handling visual question answering. 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.
What GPU do I need to run InternViT-6B?
None. InternViT-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 InternViT-6B open source?
The licensing for InternViT-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.