LongCat-2.0 makes the infrastructure story as important as the MoE scale

LongCat-2.0 is presented as a large MoE model with 1.6T total parameters and 48B active parameters. The scale is the headline number, but the HN discussion spent more durable attention on the infrastructure behind the release.

Commenters asked whether the architecture resembles other Chinese MoE lines and what the actual runtime requirements look like. One thread highlighted the official claim that training and deployment used clusters of tens of thousands of AI ASIC superpods, arguing that this may be the larger story than another parameter count.

That reading matters. LLM competition is no longer explained by benchmarks alone. Export controls, chip supply, compiler maturity, kernels, and cluster operations all shape whether a model can be trained and served. The fact that LongCat comes from Meituan’s orbit also shows how AI infrastructure work is spreading beyond classic AI labs.

Independent validation still matters. Throughput on common hardware, local inference paths, and safety behavior remain separate questions. But the community signal is clear: for frontier-scale model releases, the compute stack has become part of the story, not background plumbing.

Sources: LongCat-2.0, HN discussion.