Meituan LongCat-2.0: China's 1.6-Trillion Parameter Model Trained Entirely on Homegrown Chips
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For years, the narrative was simple: if you want to train frontier AI, you need NVIDIA GPUs. China's export-restricted access to high-end chips was supposed to be a bottleneck that would keep its models a generation behind. Meituan just made that argument obsolete. On June 30, the Beijing-based food delivery and local services giant open-sourced LongCat-2.0 — a 1.6-trillion-parameter mixture-of-experts (MoE) large language model that was trained entirely on domestic Chinese semiconductors . No…
1Key Takeaways
- For years, the narrative was simple: if you want to train frontier AI, you need NVIDIA GPUs.
- China's export-restricted access to high-end chips was supposed to be a bottleneck that would keep its models a generation behind.
- Meituan just made that argument obsolete.
- On June 30, the Beijing-based food delivery and local services giant open-sourced LongCat-2.0 — a 1.6-trillion-parameter mixture-of-experts (MoE) large language model that was trained entirely on domestic Chinese semiconductors .
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that for years, the narrative was simple: if you want to train frontier AI, you need NVIDIA GPUs.
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