Baichuan2-53B
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
- Baichuan
- Organisation type
- Industry
- Country
- China
- Published
- 9 August 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat
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
- 53B
- Training data
- tokens
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
- 8.3 × 10²³ FLOP
- How it was established
- Operation counting
Given that it was announced at a similar time to the other Baichuan2 models, this assumes that the dataset size is the same at 2.6T tokens while the parameter count was scaled up. This would be consistent with many other model releases, such as Meta's Llama models. 53b * 2.6t * 6 = 8.268e23
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
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.
- Reference
- Chinese AI startup Baichuan rolls out third LLM in four months
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Baichuan2-53B was published by Baichuan, in China, in August 2023. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Chat.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 8.3 × 10²³ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Baichuan2-53B — common questions
Who created Baichuan2-53B?
Baichuan2-53B was published by Baichuan, based in China, categorised as industry.
When was Baichuan2-53B released?
Baichuan2-53B 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.
What is Baichuan2-53B used for?
Baichuan2-53B works in Language, and is recorded as handling language modeling/generation, Chat. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Baichuan2-53B?
Around 8.3 × 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 Baichuan2-53B?
None. Baichuan2-53B 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 Baichuan2-53B open source?
No. Baichuan2-53B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Baichuan2-53B have?
Baichuan2-53B has 53B 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.
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.