MiniCPM-4-8B
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
- OpenBMB (Open Lab for Big Model Base)
- Country
- China
- Published
- 9 June 2025
- Authors
- MiniCPM Team: Chaojun Xiao, Yuxuan Li, Xu Han, Yuzhuo Bai, Jie Cai, Haotian Chen, Wentong Chen, Xin Cong, Ganqu Cui, Ning Ding, Shengdan Fan, Yewei Fang, Zixuan Fu, Wenyu Guan, Yitong Guan, Junshao Guo, Yufeng Han, Bingxiang He, Yuxiang Huang, Cunliang Kong, Qiuzuo Li, Siyuan Li, Wenhao Li, Yanghao Li, Yishan Li, Zhen Li, Dan Liu, Biyuan Lin, Yankai Lin, Xiang Long, Quanyu Lu, Yaxi Lu, Peiyan Luo,…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Code generation, Translation
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
- 8B
- Training data
- tokens
8b
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 A800 SXM
- Chips used
- 64
- Power draw
- 50.1 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
- MiniCPM4: Ultra-Efficient LLMs on End Devices
- Last updated
- 28 November 2025
What the numbers mean
What this model is
MiniCPM-4-8B was published by OpenBMB (Open Lab for Big Model Base), in the country recorded as China, during June 2025.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Code generation, Translation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
MiniCPM-4-8B — common questions
MiniCPM-4-8B— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
MiniCPM-4-8B— how many parameters does it have?
It has a parameter count of 8B. 8b. 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.
MiniCPM-4-8B— who created it?
It was published by OpenBMB (Open Lab for Big Model Base), based in China.
MiniCPM-4-8B— when was it released?
It was published in June 2025.
MiniCPM-4-8B— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Code generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MiniCPM-4-8B— what GPU do I need to run it?
None. This 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.