DeepSeek unveils mHC method to cut large-model training costs

If DeepSeek’s mHC technique really delivers more stable, scalable training at constant compute, it directly attacks one of the hard constraints in the race to AGI: how fast and cheaply you can push model scale. Architectural tweaks that improve gradient flow or effective depth without blowing up FLOPs can be just as important as new chips. The lineage here—from ResNets to Hyper-Connections to mHC—shows how much headroom still exists in rethinking network topology for large language models.

China’s model labs have strong incentives to innovate on efficiency because they face tighter access to cutting-edge GPUs. A successful mHC-style technique would let DeepSeek extract more capability from each unit of compute, partially offsetting hardware disadvantages. If the approach generalizes and is adopted widely, we could see a step-change in how quickly and cheaply new frontier models can be trained globally. Even if the gains are incremental rather than dramatic, they stack with hardware advances, nudging effective capabilities forward faster than raw FLOP curves alone would suggest.