BaGuaLu
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
- Tsinghua University,Zhejiang Lab,Beijing Academy of Artificial Intelligence / BAAI,Alibaba
- Organisation type
- Academia,Academia,Industry
- Country
- China
- Published
- 28 March 2022
- Authors
- Zixuan Ma, Jiaao He, Jiezhong Qiu, Huanqi Cao, Yuanwei Wang, Zhenbo Sun, Liyan Zheng, Haojie Wang, Shizhi Tang, Tianyu Zheng, Junyang Lin, Guanyu Feng, Zeqiang Huang, Jie Gao, Aohan Zeng, Jianwei Zhang, Runxin Zhong, Tianhui Shi, Sha Liu, Weimin Zheng, Jie Tang, Hongxia Yang, Xin Liu, Jidong Zhai, Wenguang Chen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Language modeling, Image classification
- Approach
- Self-supervised learning
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
- 173.9T
- Training data
- tokens
Table 3, MoDa-174T has 173.9 trillion parameters
17.5B tokens (in English, this is approximately 13.1B words, but the conversion may be different in Chinese) and 60.5M images.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 37
Sources
Where this record came from and when it was last checked.
- Reference
- BaGuaLu: Targeting Brain Scale Pretrained Models with over 37 Million Cores
- Last updated
- 28 November 2025
What the numbers mean
Background
BaGuaLu was published by Tsinghua University,Zhejiang Lab,Beijing Academy of Artificial Intelligence / BAAI,Alibaba, in China, in March 2022. The organisation is categorised as academia,Academia,Industry.
It works in Multimodal, Language, Vision, and is recorded as doing language modeling, Image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
BaGuaLu — common questions
Is BaGuaLu open source?
The licensing for BaGuaLu was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does BaGuaLu have?
BaGuaLu has 173.9T parameters. Table 3, MoDa-174T has 173.9 trillion 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 BaGuaLu?
BaGuaLu was published by Tsinghua University,Zhejiang Lab,Beijing Academy of Artificial Intelligence / BAAI,Alibaba, based in China, categorised as academia,Academia,Industry.
When was BaGuaLu released?
BaGuaLu was published in March 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 BaGuaLu used for?
BaGuaLu works in Multimodal, Language, Vision, and is recorded as handling language modeling, Image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run BaGuaLu?
None. BaGuaLu 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.