Domestic Chip End-to-End Training
LongCat-2.0's unique narrative: the first trillion-parameter model with training and deployment fully on domestic compute — and what that means in practice.
Training Route Comparison
| Model | Pretraining | Inference / Deployment | End-to-End Domestic? |
|---|---|---|---|
| LongCat-2.0 | 50,000-card domestic compute cluster | Domestic chips | Yes — full pipeline |
| DeepSeek V4-Pro | NVIDIA hardware (reported) | Huawei Ascend (inference stage) | Partial — inference only |
Official releases emphasize LongCat-2.0 as the industry's first model of this scale to complete both training and inference on a domestic chip stack. DeepSeek V4-Pro, by contrast, is widely reported to retain NVIDIA-based pretraining while shifting inference to Huawei chips — a meaningful but narrower claim.
What This Means for Developers
Domestic-chip training affects supply chain resilience, training–inference consistency, and iteration speed for teams deploying on alternative hardware.
- Supply chain resilience: A model trained entirely on domestic hardware is less exposed to export-control shocks on high-end GPUs during both research and production scaling.
- Training–inference consistency: When pretraining, fine-tuning, and serving share the same chip ecosystem, numerical behavior and kernel optimizations tend to align — reducing "works in lab, breaks in prod" surprises.
- Iteration velocity: Teams that control the full stack can iterate on architecture (MoE routing, sparse attention, expert fusion) without waiting for cross-vendor portability fixes between NVIDIA training clusters and alternate inference backends.
- Long-term availability: For teams deploying in regions prioritizing domestic compute, an end-to-end domestic model may offer clearer roadmap guarantees than models with split training/inference footprints.
LongCat-2.0 Technical Context
LongCat-2.0's 1.6T MoE architecture — with dynamic activation of 33B–56B tokens, native 1M context via LongCat Sparse Attention (LSA), and MOPD multi-expert fusion — was designed and validated on this domestic cluster. The same infrastructure supports ongoing post-training and serving.
- Scale: ~50,000 domestic accelerator cards for end-to-end training
- Architecture: ScMoE with zero-computation experts, token-level dynamic compute
- Context: Native 1M tokens without degrading agent benchmark performance
- Agent focus: SWE-bench Pro 59.5, Terminal-Bench 70.8
Training hardware details are based on public release notes and industry reporting. Verify against official documentation for deployment planning.