Meituan's LongCat-2.0 shows China can train massive AI models without Nvidia
Quick Answer
Meituan's LongCat-2.0, a 1.6 trillion parameter AI model, was trained entirely on domestic Chinese chips, outperforming some Western models in benchmarks.
Quick Take
Meituan's LongCat-2.0, a 1.6 trillion parameter AI model, was trained entirely on domestic Chinese chips, outperforming some Western models in benchmarks. Despite US export controls, this achievement signals China's growing capabilities in AI model training without reliance on Nvidia.
Key Points
- LongCat-2.0 trained on over 50,000 domestic AI ASICs.
- Achieved top scores on Pro (59.5) and Multilingual (77.3).
- Falls short of Claude Opus 4.7 and 4.8 in some benchmarks.
- First competitive trillion-parameter model from China using local hardware.
- Model not yet available on HuggingFace for independent verification.
📖 Reader Mode
~1 min readMeituan trains a 1.6 trillion parameter AI model entirely on Chinese chips, no Nvidia required. "LongCat-2.0 has demonstrated that we now have the capability to train large-scale models on domestic computing clusters," the Chinese company said. Training ran on a cluster of more than 50,000 domestically made AI ASICs and covered over 35 trillion tokens. The LongCat team has only existed since 2023. Its first model shipped late last year.
On some benchmarks, LongCat-2.0 beats leading Western models. On SWE-bench Pro (59.5) and SWE-bench Multilingual (77.3), it tops Gemini 3.1 Pro and GPT-5.5 but falls short of Claude Opus 4.7 and 4.8. On other tests like IFEval (90.0), IMO-AnswerBench (81.8), and GPQA-diamond (88.9), it trails Gemini and GPT-5.5 by a wide margin in some cases.
The message to Washington is hard to miss. Despite US export controls in place since 2022, China appears to have produced its first competitive trillion-parameter model trained entirely on domestic hardware. Meituan didn't name the specific chip maker, though. And the model isn't yet available on HuggingFace, making independent verification difficult.
— Originally published at the-decoder.com
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