MooER
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
- Moore Threads
- Organisation type
- Industry
- Country
- China
- Published
- 9 August 2024
- Authors
- Junhao Xu, Zhenlin Liang, Yi Liu, Yichao Hu, Jian Li, Yajun Zheng, Meng Cai, Hua Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR), Translation
- Base model
- Qwen2-7B
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
- 7.2B
- Training data
- tokens
7 B active parameters in the decoder (inherits Qwen-2-7B-instruct) + 158 M in the Paraformer audio encoder → ~ 7.2 B total.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- MTT S4000
- Chips used
- 8
- Power draw
- 7.1 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 3
Sources
Where this record came from and when it was last checked.
- Reference
- MooER: LLM-based Speech Recognition and Translation Models from Moore Threads
- Last updated
- 1 December 2025
What the numbers mean
About this model
MooER was published by Moore Threads, in the country recorded as China, during August 2024. It comes out of an organisation categorised as industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR), Translation.
Rather than being trained from scratch, it is derived from Qwen2-7B. Most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
MooER — common questions
MooER— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR), Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MooER— 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.
MooER— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
MooER— how many parameters does it have?
It has a parameter count of 7.2B. 7 B active parameters in the decoder (inherits Qwen-2-7B-instruct) + 158 M in the Paraformer audio encoder → ~ 7.2 B total. 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.
MooER— who created it?
It was published by Moore Threads, based in China, an organisation categorised as industry.
MooER— when was it released?
It was published in August 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.
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.