According to a report by STAR Market Daily, citing multiple industry sources, DeepSeek plans to use Huawei’s Ascend AI chips to pre-train its next-generation model, DeepSeek V5. This would mark a significant shift, as Ascend chips have previously been used primarily for inference rather than pre-training DeepSeek’s models.
The collaboration between DeepSeek and Huawei dates back to early 2025. Around the Chinese New Year, Huawei reportedly deployed more than 3,000 Ascend chips to support DeepSeek V3 and R1 on Huawei Cloud. Following the release of DeepSeek V4 in April 2026, Huawei announced that its Ascend SuperPoD systems could fully support the V4 model family through joint hardware-software optimisation.
The partnership has since expanded beyond inference deployment. On September 30, DeepSeek open-sourced a set of infrastructure components specifically adapted for Huawei’s Ascend platform, including compiler tools, high-performance computing libraries and distributed communication software. This suggests a deeper effort to align DeepSeek’s software stack with Huawei’s hardware architecture.
There has also been progress on the training side. A research consortium in Shenzhen recently completed full-parameter post-training of DeepSeek V4-Pro, a 1.6-trillion-parameter model, using a cluster of Huawei Ascend 910C chips. However, this was post-training rather than pre-training from scratch, which remains considerably more demanding.
If confirmed, DeepSeek’s plan to pre-train V5 on Ascend chips would represent a major milestone for China’s domestic AI ecosystem. It would provide an important test of whether Chinese AI accelerators can support the full pre-training of a frontier large language model, rather than primarily serving inference and post-training workloads.
The broader significance goes beyond DeepSeek itself. Successful pre-training on Ascend could reduce Chinese AI developers’ dependence on Nvidia hardware and strengthen Huawei’s position as a domestic alternative for advanced AI computing. However, the reported plan has not yet been independently confirmed, and its technical and commercial viability will ultimately depend on training stability, cluster performance, software maturity and the resulting model’s capabilities.