Meituan Open Sources LongCat 2.0: 1.6 Trillion Parameter MoE Model Trained on AI ASICs
Meituan releases LongCat 2.0, a 1.6 trillion parameter MoE language model with 1 million token context window, trained entirely on AI ASIC hardware. Model weights coming soon under MIT license.
On June 30, 2026, Meituan (美团) unveiled LongCat 2.0, a massive Mixture-of-Experts language model with 1.6 trillion total parameters and approximately 48 billion activated parameters per token. The model is licensed under MIT and represents one of the largest open-weight models ever released.
Model weights are not yet available — the team notes "weights coming soon" on the HuggingFace repository. This article covers the announcement and architecture.
Key Specifications
| Specification | Detail |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 1.6 trillion |
| Activated Parameters | ~48B per token |
| Context Window | 1 million tokens (LongCat Sparse Attention) |
| Training Data | 35+ trillion tokens |
| Training Hardware | AI ASIC superpods (not NVIDIA GPUs) |
| License | MIT |
Significance: AI ASIC Training
One of the most notable aspects of LongCat 2.0 is that both the full training run and large-scale deployment were built entirely on AI ASIC superpods — custom AI accelerator chips rather than NVIDIA GPUs. The pretraining spanned millions of accelerator-hours across more than 35 trillion tokens with no rollbacks or irrecoverable loss spikes, demonstrating frontier-scale training capability on alternative hardware.
Architecture Highlights
LongCat 2.0 introduces LongCat Sparse Attention, designed to handle long-context tasks efficiently. The model was trained on hundreds of billions of tokens of 1M-context data. Combined with dedicated post-training, this gives LongCat 2.0 strong performance on coding and agentic tasks.
Status
The HuggingFace repository (meituan-longcat/LongCat-2.0) is live with documentation and specifications, but model weights have not been released yet. The team has indicated weights will follow.
Links
- HuggingFace Repository
- Technical Blog Post
- License: MIT
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