LongCat-2.0, a large-scale MoE model with 1.6T total and 48B Active
longcat.chat
June 30, 2026
1 min read
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48/100
Summary
LongCat-2.0 is a large model with 1.6 trillion parameters, fully trained using domestically produced chips. The model aims to improve performance and efficiency in AI applications.
Community Sentiment
Positives
- LongCat-2.0's architecture builds upon DeepSeek's work, indicating significant advancements in model capabilities and potential for future AI developments.
- The use of large-scale clusters of AI ASIC superpods for training suggests a move towards more efficient and scalable AI infrastructures, which could reduce costs and improve performance.
- If LongCat-2.0 can operate effectively without relying on NVIDIA's ecosystem, it could pave the way for more diverse hardware solutions in AI development.
Concerns
- The model's incorrect answer to a complex question raises concerns about its reasoning capabilities and the limitations of its training data, highlighting potential issues in AI reliability.
- There are worries about the lack of a mature supporting software community for the hardware used in LongCat-2.0, which could hinder developer experience and accessibility.
- The absence of released weights and inference solutions for LongCat-2.0 limits its usability and accessibility for developers, which could slow down adoption and experimentation.