Pangu Ultra
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
- 10 April 2025
- Authors
- Yichun Yin, Wenyong Huang, Kaikai Song, Yehui Tang, Xueyu Wu, Wei Guo, Peng Guo, Yaoyuan Wang, Xiaojun Meng, Yasheng Wang, Dong Li, Can Chen, Dandan Tu, Yin Li, Fisher Yu, Ruiming Tang, Yunhe Wang, Baojun Wang, Bin Wang, Bo Wang, Boxiao Liu, Changzheng Zhang, Duyu Tang, Fei Mi, Hui Jin, Jiansheng Wei, Jiarui Qin, Jinpeng Li, Jun Zhao, Liqun Deng, Lin Li, Minghui Xu, Naifu Zhang, Nianzu Zheng, Qian…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Code generation, Language modeling/generation
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
- 135B
- Training data
- 13,200,000,000,000 tokens
- Batch size
- 8,388,608
13.2 trillion tokens
2048 * 4096
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
- 1.1 × 10²⁵ FLOP
- How it was established
- Operation counting,Comparison with other models
When compared to Llama 3.1 405B, Pangu Ultra achieves better scores on most of the challenging benchmarks, while utilizing only about 29% of the training FLOPs required by Llama 405B. Compute = 6 FLOP/token/param * 135e9 params *13.2e12 tokens = 1.069200e+25 FLOP This is consistent with 29% of Llama 405B's compute: 3.8e25*0.29=1.1e25.
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
- 8,192
- Hardware utilisation
- MFU 52.0%
- Power draw
- 6.4 MW
"Through careful tuning of load balancing across PP and VPP stages, we are able to achieve approximately 43% MFU on an 8,192 NPU cluster as a baseline." ... "Overall, we achieve over 52% Model FLOPs Utilization (MFU) when training Pangu Ultra on 8,192 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
"Our model and system will be available for our commercial customers"
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
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 16
A closer examination reveals that Pangu Ultra excels on Chinese benchmarks, surpassing both Qwen 2.5 72B and DeepSeek V3, the current best-performing Chinese model. In addition, when compared to Llama 3.1 405B, Pangu Ultra achieves better scores on most of the challenging benchmarks, while utilizing only about 29% of the training FLOPs required by Llama 405B. These results suggest the effectiveness of our model architecture and the high quality of our training data. Table 3, Tabke 4: seems that…
Sources
Where this record came from and when it was last checked.
- Reference
- Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Pangu Ultra was published by Huawei, in China, in April 2025. It comes out of industry.
It works in Language, and is recorded as doing code generation, Language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took roughly 1.1 × 10²⁵ FLOP of computation, on Huawei Ascend 910B — a measure of what producing the model cost, not of how fast it answers.
Around 13,200,000,000,000 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Pangu Ultra — common questions
Who created Pangu Ultra?
Pangu Ultra was published by Huawei, based in China, categorised as industry.
When was Pangu Ultra released?
Pangu Ultra was published in April 2025.
What is Pangu Ultra used for?
Pangu Ultra works in Language, and is recorded as handling code generation, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Pangu Ultra?
Around 1.1 × 10²⁵ FLOP, on 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.
What GPU do I need to run Pangu Ultra?
None. Pangu Ultra 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 Pangu Ultra open source?
No. Pangu Ultra has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Pangu Ultra have?
Pangu Ultra has 135B 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.