On June 5, 2026, Shenzhen announced a major piece of news: Shenzhen Hetao College, together with HIT (Shenzhen) and Huawei, used 1,000 Huawei Ascend 910C chips to successfully complete full-parameter post-training of the 1.6-trillion-parameter DeepSeek-V4-Pro large model.
This was no tentative attempt, but a milestone technological breakthrough. It proved with irrefutable engineering results that: domestic AI chips are fully capable of supporting world-class, super-large-parameter model training.
Why this matters
The two thresholds of AI chips: "inference" and "training"
- Inference: using an existing model to chat, write copy. Domestic chips could already do this
- Training: adjusting model parameters to learn new capabilities. Full-parameter training adjusts all 1.6 trillion parameters at once — maximum difficulty
Previously, full-parameter training of trillion-scale models was monopolized by NVIDIA H100/H200. Domestic chips could only do inference, not large-scale training.
The significance of this breakthrough: domestic compute leapt from "usable" to "useful", from "inference" to "training".
Technical details
Training configuration
| Item | Parameter |
|---|
| Chips | Huawei Ascend 910C × 1,000 |
| Model | DeepSeek-V4-Pro |
| Parameters | 1.6 trillion (1600B) |
| Training type | Full-parameter post-training |
| Framework | MindSpore + torch_npu |
| Completed | Announced June 5, 2026 |
| Metric | Value | Assessment |
|---|
| Compute utilization | >30% | Industrial grade (top overseas chips ~40%) |
| Key training operator efficiency | +14% | vs previous-gen 910B |
| Communication bandwidth utilization | >60% (est.) | MoE All-to-All communication |
| Stability | 1,000 cards trained continuously with no failures | Cluster stability met standard |
💡 About 30% compute utilization: many feel 30% is low, but in large-model training this is already a very respectable industrial-grade level. Even with the most advanced overseas chips, many teams' actual utilization is around 40%.
Ascend 910C detailed specs
Ascend 910C is Huawei's AI training/inference chip announced at the Huawei Analyst Conference (April 24, 2024), with a theoretical peak of 800 TFLOPS (BF16), in the same class as NVIDIA H100.
| Parameter | Ascend 910C | Ascend 910B | NVIDIA H100 |
|---|
| Architecture | Ascend 910C | Ascend 910B | Hopper |
| Process | TSMC 7nm (est.) | TSMC 7nm | TSMC 4NP |
| BF16 compute | 800 TFLOPS | 256 TFLOPS | 989 TFLOPS (sparse) |
| Memory | 64GB HBM (est.) | 64GB HBM2e (B1/B2) | 80GB HBM3 |
| Memory bandwidth | ~2TB/s (est.) | 600 GB/s (B1/B2) | 3.35 TB/s |
| TDP | ~400W (est.) | 300-400W | 700W |
| Mass production | April 2026 (full production) | Nov 2022 | Mar 2022 |
Key upgrades:
- ✅ 3× compute: from 910B's 256 TFLOPS to 800 TFLOPS
- ✅ Mature software ecosystem: torch_npu adapts PyTorch, MindSpore framework mature
- ✅ Cluster stability: 1,000 cards trained continuously with no failures (the biggest breakthrough)
Technical challenges and solutions
Challenge 1: Memory demand of trillion-scale models
A 1.6-trillion-parameter model needs, just for model parameters:
- FP16: 1.6T × 2 bytes = 3.2 TB
- Plus gradients and optimizer states: at least 10 TB of memory
Huawei's solution:
- Model Parallel: distribute the model across 1,000 910C chips
- ZeRO optimizer: optimize memory footprint
- Gradient accumulation: update parameters in stages
Challenge 2: Communication efficiency of thousand-card clusters
Training with 1,000 chips, inter-chip communication becomes the bottleneck. MoE models need All-to-All communication (each expert may need to communicate with all others).
Huawei's solution:
- HCCS (Huawei Collective Communication Scheduler): in-house high-speed interconnect protocol
- Layered communication: intra-node NVLink + inter-node HCCS
- Communication-compute overlap: data transfer concurrent with computation
Challenge 3: Training stability
Trillion-scale model training takes weeks or months; any single card failure can interrupt the entire training.
Huawei's solution:
- Fault detection and auto-recovery: real-time monitoring of card status, auto-restart and recovery on failure
- Checkpoint optimization: high-frequency training-state saves (every N steps)
- Ascend cluster management software: designed specifically for enterprise training
Competitive comparison
| Vendor | Chip | 1.6T-param training | Ecosystem maturity | Availability |
|---|
| Huawei | Ascend 910C | ✅ Completed | ⭐⭐⭐ (improving) | China-localized |
| NVIDIA | H100/H200 | ✅ Industry standard | ⭐⭐⭐⭐⭐ | Global (export-controlled) |
| AMD | MI300X | ✅ Feasible | ⭐⭐⭐⭐ | Global |
| Google | TPU v5p/8t | ✅ JAX-native | ⭐⭐⭐⭐ | Google Cloud |
Conclusion: Ascend 910C has caught up to H100 in hardware performance, still lags in software ecosystem, but this training success proves engineering feasibility.
Industry impact
1. The "Zunyi Conference" of domestic compute
This breakthrough is called the "Zunyi Conference" of domestic compute — from passive defense to strategic counteroffensive.
Specific impact:
- ✅ Breaks the bias that "domestic chips can only do inference"
- ✅ Proves domestic chips can train frontier models
- ✅ Provides compute foundation for domestic large models (e.g., DeepSeek-V4, ERNIE 5.0)
2. Impact on NVIDIA
Huawei Ascend 910C completing trillion-scale training means China's AI industry is less dependent on NVIDIA.
| Scenario | Before | Now |
|---|
| Inference | Domestic chips usable | Domestic chips useful |
| Training | Must use H100/H200 | Can use 910C |
| Large-scale training | Must use H100 clusters | Can use 910C clusters |
3. Boost to the domestic chip industry
This breakthrough will drive the entire domestic AI chip supply chain:
- Chip design: Cambricon, MetaX, Moore Threads accelerate iteration
- Wafer manufacturing: SMIC, Hua Hong get more orders
- Packaging/test: JCET, TFME benefit
Huawei Ascend roadmap (2025-2028)
| Time | Chip | Positioning |
|---|
| Q1 2025 | Ascend 910C | Flagship training/inference (mass-produced) |
| Q1 2026 | Ascend 950PR | Inference-optimized (~500 TFLOPS BF16) |
| Q4 2026 | Ascend 950DT | Data-center training |
| Q4 2027 | Ascend 960 | Next-gen flagship |
| Q4 2028 | Ascend 970 | Next-next-gen |
Training lessons shared
The Shenzhen Hetao College team accumulated valuable experience:
✅ Successes
- Progressive training: start from small models (7B), gradually scale to 1.6T
- Mixed-precision training: BF16 main + FP32 gradient accumulation
- Communication optimization: All-to-All overlap with computation
- Fault recovery: save checkpoint every 1,000 steps
⚠️ Challenges encountered
- Memory fragmentation: severe fragmentation over long training, needs periodic cleanup
- Communication bottleneck: MoE All-to-All takes 30%+ of training time
- Software bugs: torch_npu occasional memory leak, needs training process restart
References
This article is compiled from public reports. Salute to the teams at Shenzhen Hetao College, HIT (Shenzhen), and Huawei — you proved the feasibility of China's AI compute with engineering results.