When planning AI infrastructure, enterprise decision makers need clear data. This article provides a detailed comparison of Huawei Ascend 910B and Nvidia H100 SXM based on specifications, real-world benchmarks, and total cost of ownership.
Specifications Comparison
| Parameter | Ascend 910B | Nvidia H100 SXM |
|---|---|---|
| FP16 Performance | 376 TFLOPS | 989 TFLOPS |
| BF16 Performance | 376 TFLOPS | 989 TFLOPS |
| INT8 Performance | 640 TOPS | 3,958 TOPS |
| Memory | 64 GB HBM2e | 80 GB HBM3 |
| Memory Bandwidth | 1.6 TB/s | 3.35 TB/s |
| Power Consumption | 400W | 700W |
| Price (MSRP) | ~$15,000 | ~$30,000 |
On paper, H100 leads in raw performance. But real-world AI workloads tell a different story.
Real-World Benchmarks: Llama 3 70B Inference
| Metric | Ascend 910B | H100 | Difference |
|---|---|---|---|
| Throughput | 85 tok/s | 95 tok/s | -11% |
| Latency (TTFT) | 180 ms | 150 ms | +20% |
| Latency (TBT) | 45 ms | 38 ms | +18% |
| Memory Usage | 58 GB | 62 GB | -6% |
For most enterprise inference workloads, the performance difference is negligible. Both chips handle large language models efficiently.
Total Cost of Ownership (3 Years)
Configuration: 8× NPU server for enterprise deployment
| Cost Item | Ascend 910B | H100 |
|---|---|---|
| Hardware | 3,200,000 CZK | 6,800,000 CZK |
| Power (3 years) | 420,000 CZK | 735,000 CZK |
| Support (3 years) | 480,000 CZK | 1,020,000 CZK |
| Total | 4,100,000 CZK | 8,555,000 CZK |
Savings with Ascend: 52% — Over 4 million CZK difference over 3 years.
When to Choose Ascend
✅ Suitable for:
- Inference workloads
- Chinese LLMs (DeepSeek, Qwen)
- Limited budget
- Need fast delivery (no waiting)
- Independence from US supply chains
❌ Not suitable for:
- Training largest models (400B+)
- Legacy CUDA codebase
- Need for cutting-edge research features
Migration Complexity
| Workload | Complexity | Time |
|---|---|---|
| ONNX model inference | Low | 1–2 days |
| PyTorch (via CANN) | Medium | 1 week |
| Custom CUDA kernels | High | 2–4 weeks |
| Distributed training | Medium | 1–2 weeks |
Conclusion
Ascend 910B is not for everyone. But for most enterprise deployments, it offers sufficient performance at half the cost. The decision should be based on your specific use case, not marketing materials.
For inference workloads with Chinese LLMs or budget constraints, Ascend is a compelling alternative. For cutting-edge training research with CUDA dependencies, Nvidia remains the standard.
Need help deciding? Contact us for a consultation based on your specific requirements.