DeepSeek reportedly completes major training run on Huawei chips, bypassing Nvidia

DeepSeek has reportedly achieved a significant milestone for China's domestic AI hardware: full-parameter post-training of its V4-Pro model — comprising approximately 1.6 trillion parameters — completed using more than 1,000 Huawei Ascend 910C chips, in collaboration with Huawei Technologies and Shenzhen-based institutions, and without reliance on Nvidia hardware.

The technical significance is that post-training (not just inference) on a frontier-scale model has historically demanded Nvidia's CUDA ecosystem and high-bandwidth interconnects. Demonstrating that locally produced Ascend accelerators can sustain training-class workloads at this scale undercuts the assumption that U.S. export controls leave Chinese labs permanently dependent on smuggled or downgraded Nvidia parts.

The development arrives amid a broader narrative of DeepSeek's momentum: r/DeepSeek threads celebrated 'Deepseek v4 Pro on Claude Code,' and one viral post described a company replacing Claude with DeepSeek (743 upvotes). Routing startups like OpenRouter report DeepSeek surging as a cheaper frontier alternative. Caveats remain — the report is sourced largely from secondary outlets and a YouTube short, the efficiency and yield of the Ascend run versus Nvidia equivalents is unverified, and full-parameter post-training is less compute-intensive than from-scratch pre-training. Still, if confirmed, it marks a meaningful step in decoupling China's AI stack from U.S. silicon.