Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司)
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
- China Telecom
- Organisation type
- Industry
- Country
- China
- Published
- 18 July 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Image generation
- Task
- Language modeling/generation, Image completion, Question answering, Image generation, Image captioning, Text-to-image, Table tasks
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.
- Training data
- tokens
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- CAC registry
- Last updated
- 11 February 2026
What the numbers mean
Background
Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) was published by China Telecom, in China, in July 2024. industry is the category the publisher falls under.
It works in Multimodal, Language, Vision, Image generation, and is recorded as doing language modeling/generation, Image completion, Question answering, Image generation, Image captioning, Text-to-image, Table tasks.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) — common questions
Is Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) open source?
No. Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) have?
No parameter count has been published for Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司), which is why no memory or speed figure appears on this page.
Who created Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司)?
Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) was published by China Telecom, based in China, categorised as industry.
When was Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) released?
Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) was published in July 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 Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) used for?
Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) works in Multimodal, Language, Vision, Image generation, and is recorded as handling language modeling/generation, Image completion, Question answering, Image generation, Image captioning, Text-to-image, Table tasks. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司)?
None. Xingchen Multimodal Model (中电信人工智能科技（北京）有限公司) 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.