Source: https://mp.weixin.qq.com/s/R-1u2exLwZMOu7AITyU5kg A reporter from STAR Market Daily has learned from multiple sources within the industry supply chain that DeepSeek’s next-generation large model, DeepSeek V5, is planned to be pre-trained using Ascend chips. Previously, Huawei’s Ascend chips were primarily utilized for inference tasks for DeepSeek’s large models. According to publicly available information, the first large-scale deployment of DeepSeek models on the Ascend platform took place around the 2025 Spring Festival. In his book The Rise of Ascend, Fang Xingdong—Executive Dean of the College of Media and International Culture at Zhejiang University—notes that on January 28, 2025, Huawei’s senior leadership issued a directive for Huawei Cloud to provide rapid support to DeepSeek. Between Lunar New Year’s Eve and the second day of the Lunar New Year, Huawei and DeepSeek completed the deployment and verification of over 3,000 Ascend chips, successfully hosting the DeepSeek-V3/R1 models at Huawei Cloud’s Guian Data Center. The book further mentions that the explosive growth of DeepSeek greatly energized Ren Zhengfei; throughout the Spring Festival, he personally directed Huawei Cloud and relevant product teams to manage the massive surge in traffic. In January of the same year, SiliconFlow and Huawei Cloud launched inference services for DeepSeek-V3 and R1 based on the Ascend platform. Following the release of DeepSeek-V4 in April 2026, Huawei stated that, through the "synergy between chips and models," its full range of Ascend "super-node" products could provide comprehensive support for the DeepSeek V4 model series. On its official API page, DeepSeek noted that service throughput for the V4-Pro model remains constrained by the availability of high-end computing power; however, it anticipates a significant price reduction for the Pro version once the Ascend 950 super-node series enters mass-market availability in the second half of the year. On September 30, DeepSeek announced the open-source release of infrastructure components tailored for the Huawei Ascend computing platform. These include the TileLang high-level language compilation tool, high-performance computing libraries (such as DeepGEMM, FlashMLA, TileKernel, and DeepSelect), and the distributed communication library DeepEP—all of which correspond to the open-source components previously released for the NVIDIA platform. Huawei has provided a jointly defined "Ascend SuperPoD Flex" architecture and a UBL128 networking solution, while also opening up the Ascend C programming interface and low-level instruction set to support TileLang. This is significant because DeepSeek is not merely using Ascend for inference; it is systematically aligning with and co-developing the Ascend software stack at a foundational level—extending adaptation to the very infrastructure that underpins both training and inference. Currently, the deployment of domestic computing power is primarily concentrated in inference, with further progress still required in training. Recently, a project team—comprising the AI Training Platform at Shenzhen Hetao College, Harbin Institute of Technology (Shenzhen), the Shenzhen Big Data Research Institute, and relevant teams from Huawei—leveraged an AI computing cluster based on the domestic Ascend 910C processor to complete the full-parameter post-training of DeepSeek-V4-Pro, a large model with 1.6 trillion parameters. It should be noted that this training process did not include the initial pre-training phase. While the large-scale validation of domestic chips for inference has bolstered industry confidence, the costs associated with migrating ecosystems for training remain a critical variable that will determine the market's ultimate potential. If the next generation of DeepSeek’s large models is pre-trained using Huawei’s Ascend computing platform, it will—for the first time—provide an answer to a question that has long been met only with skepticism: "Can China’s top-tier large models be pre-trained on domestic computing hardware?"   submitted by   /u/hedgehog0 [link]   [comments]