PanGu-Σ
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 Noah's Ark Lab
- Organisation type
- Industry
- Country
- China
- Published
- 20 March 2023
- Authors
- Xiaozhe Ren, Pingyi Zhou, Xinfan Meng, Xinjing Huang, Yadao Wang, Weichao Wang, Pengfei Li, Xiaoda Zhang, Alexander Podolskiy, Grigory Arshinov, Andrey Bout, Irina Piontkovskaya, Jiansheng Wei, Xin Jiang, Teng Su, Qun Liu, Jun Yao
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, Translation, Question answering
- Approach
- Self-supervised learning
- Numerical format
- FP16
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
- 1.1T
- Training data
- 329,000,000,000 tokens
- Epochs
- 1
- Batch size
- 524,288
"In this work, we present PanGu-Σ , a large language model with sparse architecture containing 1.085 trillion parameters."
329B tokens ~= 247B words
"We train PanGu-Σ with global batch size of 512 with sequence length of 1024 for each sample"
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
- 4.7 × 10²³ FLOP
- How it was established
- Hardware
It has sparse architecture, so we can't use C=6ND. "We develop PanGu-Σ model under the framework of MindSpore and train it on a cluster with only 512 Ascend 910 AI Accelerators with 329 billion tokens over 100 days." 100 days * 512 processors * 320 teraFLOPS/processor * 33% utilization = 4.67e+23 FLOP https://www.wolframalpha.com/input?i=100+days+*+512+*+320+terahertz+*+0.33
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 910
- Chips used
- 512
- Chip-hours
- 1,228,800
- Wall-clock time
- 2,400 hours (100 days)
- Power draw
- 316.5 kW
We develop PanGu-Σ model under the framework of MindSpore 5 and train it on a cluster with only 512 Ascend 910 AI Accelerators [28] with 329 billion tokens over 100 days.
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
- Unreleased
- 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
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 78
"Our experimental findings show that PanGu-{\Sigma} provides state-of-the-art performance in zero-shot learning of various Chinese NLP downstream tasks." "The results on WMT20 translation task. PanGu-Σ outperforms previous Chinese-English SOTA pre-trained large model with a large margin." " The PanGu-Σ outperforms the current state-of-the-art model PanGu-Coder by 1.4 point on the pass@1 for MBPP tasks" "Table 5: Zero-shot results of Chinese downstream tasks. Compared to ERNIE 3.0 Titan, PanGu…
Sources
Where this record came from and when it was last checked.
- Reference
- PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
- Last updated
- 6 January 2026
What the numbers mean
About this model
PanGu-Σ was published by Huawei Noah's Ark Lab, in China, in March 2023. It comes out of industry.
It works in Language, and is recorded as doing code generation, Language modeling, Translation, Question answering.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 4.7 × 10²³ FLOP of arithmetic, on Huawei Ascend 910, which is a statement about the training budget rather than about inference.
It was trained on about 329,000,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
PanGu-Σ — common questions
Who created PanGu-Σ?
PanGu-Σ was published by Huawei Noah's Ark Lab, based in China, categorised as industry.
When was PanGu-Σ released?
PanGu-Σ was published in March 2023. 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 PanGu-Σ used for?
PanGu-Σ works in Language, and is recorded as handling code generation, Language modeling, Translation, Question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train PanGu-Σ?
Around 4.7 × 10²³ FLOP, on Huawei Ascend 910. 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-Σ?
None. PanGu-Σ 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-Σ open source?
No. PanGu-Σ has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PanGu-Σ have?
PanGu-Σ has 1.1T parameters. "In this work, we present PanGu-Σ , a large language model with sparse architecture containing 1.085 trillion 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.