Pangu Ultra MoE
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
- Huawei
- Organisation type
- Industry
- Country
- China
- Published
- 7 May 2025
- Authors
- Yehui Tang, Yichun Yin, Yaoyuan Wang, Hang Zhou, Yu Pan, Wei Guo, Ziyang Zhang, Miao Rang, Fangcheng Liu, Naifu Zhang, Binghan Li, Yonghan Dong, Xiaojun Meng, Yasheng Wang, Dong Li, Yin Li, Dandan Tu, Can Chen, Youliang Yan, Fisher Yu, Ruiming Tang, Yunhe Wang, Botian Huang, Bo Wang, Boxiao Liu, Changzheng Zhang, Da Kuang, Fei Liu, Gang Huang, Jiansheng Wei, Jiarui Qin, Jie Ran, Jinpeng Li, Jun Zh…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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
- 718B
- Training data
- tokens
a sparse LLM with 718 billion parameters 39B activated parameters
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
- 3.1 × 10²⁴ FLOP
- How it was established
- Operation counting
Speculatively assuming they trained MoE model on the same amount of tokens as Pangu Ultra (13,2 T): 6 FLOP / parameter / token * 39 * 10^9 activated parameters * 13,2 * 10^12 tokens = 3.0888e+24 FLOP
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
- Huawei Ascend 910B
- Chips used
- 6,000
- Hardware utilisation
- MFU 30.0%
- Power draw
- 4.7 MW
"Taking advantage of our model selection strategy and parallel computing system optimization, when training Pangu Ultra MoE, with performance comparable to DeepSeek R1 [10], we achieve a Model Flops Utilization (MFU) of 30.0% and Tokens Per Second (TPS) of 1.46M on 6K Ascend NPUs, compared to the baseline MFU of 18.9% and TPS of 0.61M on 4K Ascend NPUs."
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.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Pangu Ultra MoE was published by Huawei, in the country recorded as China, during May 2025. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required arithmetic totalling around 3.1 × 10²⁴ FLOP, on hardware recorded as Huawei Ascend 910B. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Pangu Ultra MoE — common questions
Pangu Ultra MoE— 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.
Pangu Ultra MoE— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Pangu Ultra MoE— how many parameters does it have?
It has a parameter count of 718B. a sparse LLM with 718 billion parameters 39B activated 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.
Pangu Ultra MoE— who created it?
It was published by Huawei, based in China, an organisation categorised as industry.
Pangu Ultra MoE— when was it released?
It was published in May 2025.
Pangu Ultra MoE— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Pangu Ultra MoE— how much compute was used to train it?
Training consumed around 3.1 × 10²⁴ FLOP, on hardware recorded as Huawei Ascend 910B. 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.
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.