Inside a Chinese AI Lab’s Training Stack

Constraints breed creativity, and nowhere is that clearer than in how China’s top labs approach large-scale training.

Doing more with less

Facing tighter access to the newest accelerators, several labs have leaned into aggressive quantization, custom communication kernels, and mixture-of-experts designs that activate only a fraction of parameters per token.

• Heavy use of MoE to cut active compute
• Communication-optimized training across large clusters
• Data curation treated as a first-class research problem

Why it matters globally

Many of these efficiency techniques are being published openly, and Western labs are adopting them. The constraint has, ironically, produced tooling the whole field benefits from.