AliceMind-MMU
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
- Alibaba
- Organisation type
- Industry
- Country
- China
- Published
- 17 November 2021
- Authors
- Ming Yan, Haiyang Xu, Chenliang Li, Junfeng Tian, Bin Bi, Wei Wang, Weihua Chen, Xianzhe Xu, Fan Wang, Zheng Cao, Zhicheng Zhang, Qiyu Zhang, Ji Zhang, Songfang Huang, Fei Huang, Luo Si, Rong Jin
What it does
The problem areas the model was built for. A model can carry several of each.
- Task
- Image Understanding
- Approach
- Supervised fine-tuning (SFT),Supervised
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
- 12,649,000 tokens
- Epochs
- 30
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
- 8
- Power draw
- 6.5 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
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
- Historical significance,SOTA improvement
- Record confidence
- Likely
Was the top of the VQA Challenge Leaderboard for the standard test, scoring 81.26, surpassing both human and previous record-holder.
Sources
Where this record came from and when it was last checked.
- Reference
- Achieving human parity on visual question answering
- Last updated
- 30 June 2026
What the numbers mean
Where it came from
AliceMind-MMU was published by Alibaba, in China, in November 2021. industry is the category the publisher falls under.
and is recorded as doing image Understanding.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
It was trained on about 12,649,000 tokens of text.
Its inclusion criterion is historical significance,SOTA improvement.
Answers
AliceMind-MMU — common questions
Is AliceMind-MMU open source?
No. AliceMind-MMU has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AliceMind-MMU have?
No parameter count has been published for AliceMind-MMU, which is why no memory or speed figure appears on this page.
Who created AliceMind-MMU?
AliceMind-MMU was published by Alibaba, based in China, categorised as industry.
When was AliceMind-MMU released?
AliceMind-MMU was published in November 2021. 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 AliceMind-MMU used for?
and is recorded as handling image Understanding. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run AliceMind-MMU?
None. AliceMind-MMU 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.