# LongCat-2.0
Tech Blog 📄
## Model Introduction
We introduce LongCat-2.0, a large-scale MoE language model with **1.6 trillion total parameters** and ~48 billion activated per token — a substantial step up from previous LongCat models, accompanied by several architectural improvements.
Both the full training run and the large-scale deployment are built entirely on **AI ASIC superpods**. Pretraining spans millions of accelerator-hours across more than 35 trillion tokens, with no rollbacks or irrecoverable loss spikes — demonstrating that we have the capability to conduct frontier-scale training on alternative hardware platforms.
To strengthen the model on long-horizon tasks, we introduce LongCat Sparse Attention and train LongCat-2.0 on hundreds of billions of tokens of **1M-context** data. Together with dedicated post-training, this gives LongCat-2.0 strong performance on coding and agentic tasks.
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> [!NOTE]
> 🏋️ **Model weights coming soon** — stay tuned!
