BlueLM 130B
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
- vivo AI lab
- Organisation type
- Industry
- Country
- China
- Published
- 2 November 2023
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, Question answering
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
- 130B
- Training data
- tokens
1000B Text data 10B Image data 100M video data 100M Knowledge graph (from the conference handout)
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
- 7.8 × 10²³ FLOP
- How it was established
- Operation counting
6ND = 6*130B*1000B=7.8e+23
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
- Training code
- Unreleased
information about the model is from their paper catalogue and not found on the internet
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
- Confident
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
BlueLM 130B was published by vivo AI lab, in China, in November 2023. industry is the category the publisher falls under.
It works in Language, and is recorded as doing chat, Language modeling/generation, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took roughly 7.8 × 10²³ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Answers
BlueLM 130B — common questions
How many parameters does BlueLM 130B have?
BlueLM 130B has 130B 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 BlueLM 130B?
BlueLM 130B was published by vivo AI lab, based in China, categorised as industry.
When was BlueLM 130B released?
BlueLM 130B was published in November 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.
What is BlueLM 130B used for?
BlueLM 130B works in Language, and is recorded as handling chat, Language modeling/generation, Question answering. 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 BlueLM 130B?
Around 7.8 × 10²³ 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 BlueLM 130B?
None. BlueLM 130B 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 BlueLM 130B open source?
No. BlueLM 130B has not had its weights published, so it exists only as a service controlled by its owner.
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.