Xiaoice
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
- Microsoft Research Asia
- Organisation type
- Industry
- Country
- China
- Published
- 14 September 2019
- Authors
- Li Zhou, Jianfeng Gao, Di Li, Heung-Yeung Shum
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Vision, Multimodal
- Task
- Chat, Image captioning, Visual question answering, Language modeling/generation, Translation
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)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
- Last updated
- 28 November 2025
What the numbers mean
Background
Xiaoice was published by Microsoft Research Asia, in China, in September 2019. It comes out of industry.
It works in Language, Vision, Multimodal, and is recorded as doing chat, Image captioning, Visual question answering, Language modeling/generation, Translation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Xiaoice — common questions
Who created Xiaoice?
Xiaoice was published by Microsoft Research Asia, based in China, categorised as industry.
When was Xiaoice released?
Xiaoice was published in September 2019. 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 Xiaoice used for?
Xiaoice works in Language, Vision, Multimodal, and is recorded as handling chat, Image captioning, Visual question answering, Language modeling/generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Xiaoice?
None. Xiaoice 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 Xiaoice open source?
No. Xiaoice has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Xiaoice have?
No parameter count has been published for Xiaoice, which is why no memory or speed figure appears on this page.
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.