Luca 2.0
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
- Mianbi Intelligence
- Organisation type
- Industry
- Country
- China
- Published
- 29 August 2023
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
- tokens
https://www.leiphone.com/category/ai/23kbzQXj60xZgUgO.html English translation: "Li Dahai: From a technical point of view, the CPM2 (Chinese Pretrained Model) 100 billion model we launched at that time was a sparse model of MoE, which is different from the 100 billion model we are promoting now." This suggests it is a dense model.
"Assume Chinchilla-optimal dataset size" from training compute notes
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
- 1.2 × 10²⁴ FLOP
- How it was established
- Operation counting
Assume Chinchilla-optimal dataset size: 20 * 100B * 100B * 6 = 1.2e24 FLOP
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
- Hosted access (no API)
- Training code
- Unreleased
could be accessible via API, though I have not found documentation here is accessible for chatting: https://luca.cn/home
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
- Record confidence
- Likely
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
Luca 2.0 was published by Mianbi Intelligence, in the country recorded as China, during August 2023. The category the publisher falls under is industry.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 1.2 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Luca 2.0 — common questions
Luca 2.0— who created it?
It was published by Mianbi Intelligence, based in China, an organisation categorised as industry.
Luca 2.0— when was it released?
It was published in August 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Luca 2.0— how much compute was used to train it?
Training consumed around 1.2 × 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.
Luca 2.0— 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.
Luca 2.0— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Luca 2.0— how many parameters does it have?
It has a parameter count of 100B. https://www.leiphone.com/category/ai/23kbzQXj60xZgUgO.html English translation: "Li Dahai: From a technical point of view, the CPM2 (Chinese Pretrained Model) 100 billion model we launched at that time was a sparse model of MoE, which is different from the 100 billion model we are promoting now." This suggests it is a dense model. 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.
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.