Yi-Large
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
- 01.AI
- Organisation type
- Industry
- Country
- China
- Published
- 13 May 2024
- Authors
- Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, Zonghong Dai
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat, Language modeling/generation
- Numerical format
- FP8
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
- 100B
- Training data
- 3,000,000,000,000 tokens
"Yi-Large is a software over-the-air-driven closed-source large model with a parameter of over 100 billion tokens." from https://www.chinadaily.com.cn/a/202405/13/WS6641abd1a31082fc043c6ccd.html
3T tokens for previous Yi models: "Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more."
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
- 1.8 × 10²⁴ FLOP
- How it was established
- Operation counting
6ND = 6*100000000000*3000000000000=1.8e+24 (speculative confidence because training dataset size is very uncertain)
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Data centre
- There is no paper to reference, no information about hardware used for training found in media.
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
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Yi-Large was published by 01.AI, in China, in May 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing chat, Language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took roughly 1.8 × 10²⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 3,000,000,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: training cost.
Answers
Yi-Large — common questions
When was Yi-Large released?
Yi-Large was published in May 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 Yi-Large used for?
Yi-Large works in Language, and is recorded as handling chat, Language modeling/generation. 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 Yi-Large?
Around 1.8 × 10²⁴ FLOP. 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 Yi-Large?
None. Yi-Large 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 Yi-Large open source?
No. Yi-Large has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Yi-Large have?
Yi-Large has 100B parameters. "Yi-Large is a software over-the-air-driven closed-source large model with a parameter of over 100 billion tokens." from https://www.chinadaily.com.cn/a/202405/13/WS6641abd1a31082fc043c6ccd.html. 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 Yi-Large?
Yi-Large was published by 01.AI, based in China, categorised as industry.
Source
Record last updated 28 November 2025
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.