面壁智能联合清华等开源中国首个基于华为昇腾训练的 1.58-bit 端侧大模型 BitCPM-CANN

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FaceWall Intelligence Collaborates with Tsinghua University to Open Source China's First 1.58-bit Edge-side Large Model Trained on Huawei Ascend: BitCPM-CANN

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TL;DR · AI Summary

FaceWall Intelligence and partners have released BitCPM-CANN, the first 1.58-bit edge-side large model trained entirely on Huawei Ascend in China, achieving up to 6x memory savings while maintaining over 90% performance.

Key Takeaways

BitCPM-CANN is the first 1.58-bit edge-side large model trained and open-sourced
It reduces inference memory usage by approximately 6 times compared to BF16 prec
Available in four sizes from 0.5B to 8B parameters, enabling deployment on mains

Outline

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§Project Background and Significance
BitCPM-CANN is China’s first ternary (1.58-bit) large model fully trained and open-sourced on a domestic computing platform (Huawei Ascend).
·Core Technical Features
Uses 1.58-bit ternary quantization and completes end-to-end training natively on the Ascend platform.
·Performance Metrics
Achieves ~6x memory saving during inference compared to BF16 while maintaining 90%–97.2% model retention rate.
·Engineering Infrastructure
Built upon MindSpeed × Megatron-LM for a complete low-bit training infrastructure tailored for Ascend.
·Model Specifications and Availability
Comes in four versions (0.5B, 1B, 3B, 8B), now open-sourced on HuggingFace and ModelScope.

Mindmap

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BitCPM-CANN 模型
- 技术特性
  - 1.58-bit 三值量化
  - 昇腾平台原生训练
- 性能优势
  - 显存节省6倍
  - 能力保留率90%-97.2%
- 应用场景
  - 移动端部署
  - 多尺寸支持

Highlights

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This marks the first ternary (1.58-bit) large model fully trained and open-sourced on a domestic computing platform (Huawei Ascend) in China.
— Paragraph 1
⬇︎ 下载 PNG 𝕏 分享到 X
Compared to traditional BF16 precision, BitCPM-CANN releases about 6x memory savings at inference stage while maintaining model capability retention between 90%–97.2%
— Paragraph 2
⬇︎ 下载 PNG 𝕏 分享到 X
An 8B parameter BitCPM-CANN model can be easily deployed on current flagship smartphones.
— Paragraph 3
⬇︎ 下载 PNG 𝕏 分享到 X

#Large Model#Low-bit Quantization#Huawei Ascend#Edge AI#Open-source Model

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FaceWall Intelligence Collaborates with Tsinghua University and Others to Open Source China's First 1.58-bit Edge-side Large Model Trained on Huawei Ascend: BitCPM-CANN - IT Home

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Home>Intelligent Era>Artificial Intelligence

FaceWall Intelligence Collaborates with Tsinghua University and Others to Open Source China's First 1.58-bit Edge-side Large Model Trained on Huawei Ascend: BitCPM-CANN

May 25, 2026 11:48:30 Source: IT Home Author: Qincang (Intern) Editor: Qincang

Comments: 4

IT Home May 25 News - FaceWall Intelligence, in collaboration with Tsinghua University and the OpenBMB open-source community, today officially released and open-sourced their latest achievement in low-bit large model training - BitCPM-CANN.

According to official statements, this is China's first ternary (1.58-bit) large model that is completely trained end-to-end and open-sourced based on a domestic computing platform (Huawei Ascend). From quantization operators, training algorithms to the full-link framework, BitCPM-CANN was natively completed on Huawei Ascend, including four model sizes of 0.5B, 1B, 3B, and 8B. Compared with the same-size MiniCPM4 full-precision family through item-by-item evaluation, it demonstrates excellent performance.

Image 10: FaceWall Intelligence Collaborates with Tsinghua University and Others to Open Source China's First 1.58-bit Edge-side Large Model Trained on Huawei Ascend: BitCPM-CANN

Compared to traditional BF16 precision, BitCPM-CANN releases approximately 6 times memory benefits during inference while maintaining model capability retention rate at 90%-97.2%.

Image 11: FaceWall Intelligence Collaborates with Tsinghua University and Others to Open Source China's First 1.58-bit Edge-side Large Model Trained on Huawei Ascend: BitCPM-CANN

According to officials, for the mobile phone industry, 6 times memory benefits mean that an 8B parameter BitCPM-CANN large model can easily run on current mainstream flagship phones.

In addition, FaceWall Intelligence has built a complete low-bit training infrastructure based on the MindSpeed × Megatron-LM backbone, including environment adaptation, 32K long sequence support, parallel strategies, fused operators, and other complete engineering systems. From now on, all low-bit training work targeting Ascend can be built on the same set of public infrastructure.

The full series of BitCPM-CANN 0.5B / 1B / 3B / 8B model weights are now open-sourced. IT Home provides the links below:

HuggingFace: https://huggingface.co/collections/openbmb/bitcpm-cann

ModelScope: https://modelscope.cn/collections/OpenBMB/BitCPM-CANN

Advertisement Statement: The external jump links contained in the text (including but not limited to hyperlinks, QR codes, passwords, etc.) are used to convey more information, save selection time, and results are for reference only. All IT Home articles contain this statement.

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