What China’s AI Labs Learned from Scaling Domestic Large‑Model Training

With the rapid emergence of ChatGPT, large‑model artificial intelligence has become a focal point across industries, prompting a surge of domestic and international models. While progress is evident, Chinese large‑model development still confronts significant technical and operational hurdles.

The piece outlines the computational traits of domestic large‑model training, covering platform specifics, system‑level challenges, operator implementation, fault‑tolerance mechanisms, and the parallelism support offered by major frameworks. It also looks ahead to emerging algorithms that could shape future model efficiency.

To address these obstacles, the author proposes six coordinated strategies:

Scale up compute resources by accelerating the “East‑Data‑West‑Compute” initiative, expanding compute‑network infrastructure, and reducing latency.

Strengthen data governance through national standards, clear usage rules, and interoperable industry data to ensure high‑quality training datasets.

Establish a national large‑model R&D team that consolidates top talent and resources, mitigating the fragmented “small‑and‑scattered” development model.

Create a dedicated national large‑model fund to finance research, training, and related activities.

Introduce favorable tax policies and allow double‑counting of state‑owned enterprise investments in model development as net profit.

Invest heavily in core technologies, especially domestic AI‑chip design and manufacturing, to resolve the “bottleneck” in hardware.

These recommendations aim to leverage China’s institutional strengths, enhance top‑level design, and shift the nation from merely following global AI trends to leading them.