DeepSeek V5 Planned to be Pre-trained on Huawei Ascend Chips: Accelerating AI Hardware Localization
By Mr.Xu Community Post
Published:
Summary:DeepSeek plans to pre-train its next-generation large model, DeepSeek V5, using Huawei's Ascend chips, marking the first attempt by a top-tier Chinese AI model to leverage domestic computing hardware for comprehensive training. Previously, Ascend chips were primarily used for inference tasks in DeepSeek models. This move highlights the growing potential of AI hardware localization, as Huawei's Ascend platform offers comprehensive support from chips to software stacks, including TileLang and Deep
Key Breakthrough
DeepSeek plans to pre-train its next-generation large model, DeepSeek V5, using Huawei's Ascend chips. This marks a significant step in the localization of AI computing power in China, as Ascend chips were previously primarily used for inference tasks in DeepSeek models. This expansion to pre-training highlights the growing importance of domestic AI hardware.
Technical Mechanism Analysis
The Huawei Ascend platform offers comprehensive support from chips to software stacks, showcasing its potential in AI infrastructure. Key components include:
- TileLang: A high-level language compilation tool for optimizing AI model compilation.
- DeepGEMM, FlashMLA, TileKernel, DeepSelect: A suite of high-performance computing libraries for accelerating matrix operations and model training.
Additionally, Huawei provides the Ascend SuperPoD Flex architecture, UBL128 networking solutions, and opens up the Ascend C programming interface and low-level instruction set to support TileLang. These technologies form a complete AI training ecosystem.
Engineering Trade-offs and Performance
While domestic chips have proven effective for inference, the migration cost of the training ecosystem remains a critical challenge. The full-parameter post-training of the DeepSeek V4-Pro model on the Ascend 910C processor cluster demonstrates the capability to train a 1.6 trillion parameter model, but the initial pre-training phase has yet to be addressed. This indicates that further optimization is needed for Ascend to meet the demands of large-scale pre-training.
Developer Implementation and Deployment Recommendations
For developers, the openness and comprehensive support of the Ascend platform provide new options for AI model training and inference. However, in practical applications, developers need to consider the following factors:
- Hardware Compatibility: Ensure model architecture is compatible with Ascend chips.
- Software Stack Support: Leverage Ascend's software libraries and tools to optimize model performance.
- Migration Cost: Assess the cost and benefits of migrating from existing platforms to Ascend.
Future Outlook
If the pre-training of DeepSeek V5 on the Ascend platform is successful, it will set an important milestone for AI computing power localization and provide valuable experience and reference for other AI models.
Conclusion
The collaboration between DeepSeek and Huawei demonstrates the great potential of AI computing power localization and also reveals the current challenges. With the continuous advancement of technology and the gradual improvement of the ecosystem, domestic AI hardware is expected to play a greater role in the future development of AI.
— END —Source: Reddit r/LocalLLaMA (2026-11-03)
Tags: #DeepSeek #Ascend Chips #AI Hardware Localization
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