LongCat-2.0, a large-scale MoE model with 1.6T total and 48B Active (longcat.chat)

The introduction of LongCat-2.0 marks a significant advancement in the AI/ML landscape, featuring a large-scale mixture of experts (MoE) model with 1.6 trillion parameters and approximately 48 billion activated per token. This model is engineered for enhanced long-context processing and boasts architectural innovations, including LongCat Sparse Attention, which improves efficiency for long-horizon tasks. Built on AI ASIC superpods, LongCat-2.0 showcases the capacity for large-scale training without fluctuations, thanks to optimizations in both its physical infrastructure and model design. LongCat-2.0's integration with major frameworks like Claude Code and Hermes enhances its usability across various coding and task execution applications, providing developers with a robust tool for collaborative projects. Its unique features, such as the N-gram Embedding module and the MOPD (Multi-Expert Post-Deployment) architecture, allow for better parameter efficiency and improved performance in complex problem-solving scenarios. The model is finely tuned for real-world tasks, demonstrating strong capabilities in code understanding and logical reasoning, setting a new standard in the efficiency and effectiveness of large language models in real-world applications.