Cambricon MLU690 vs NVIDIA H100: In-Depth Comparison — Can a Domestic AI Chip Replace the H100?
· 6 min read
In 2026, against the backdrop of U.S. export controls on AI chips to China, Cambricon's MLU690 has drawn intense attention as a "China-made H100." This article compares the two in depth across compute, memory, power, software ecosystem, measured performance, and price to help you make a selection decision.
Core Verdict (Read This First)
| Dimension | MLU690 | H100 | Winner | Gap |
|---|---|---|---|---|
| BF16 compute | 600 TFLOPS | 989 TFLOPS | H100 | +65% |
| Memory capacity | 64GB HBM3 | 80GB HBM3 | H100 | +25% |
| Memory bandwidth | 2 TB/s | 3.35 TB/s | H100 | +68% |
| TDP | 280W | 700W | MLU690 | -60% |
| Energy efficiency | 2.14 TFLOPS/W | 1.41 TFLOPS/W | MLU690 | +52% |
| Software ecosystem | NeuWare (~75% coverage) | CUDA (100% coverage) | H100 | large gap |
| Price | ~¥140,000 | ~¥200,000 | MLU690 | -30% |
| Availability | domestic spot stock | export-controlled | MLU690 | ✅ |
One-line summary: MLU690 delivers roughly 60% of H100's compute, but at only 40% of the power and 70% of the price — a strong fit for AI training and inference in the Chinese market.
1. Detailed Spec Comparison
1.1 Compute
| Precision | MLU690 | H100 SXM5 | H200 SXM5 | Note |
|---|---|---|---|---|
| FP8 | ~300 TFLOPS (est.) | 3,958 TFLOPS | 3,958 TFLOPS | H100 supports FP8; MLU690 likely does not |
| BF16/FP16 | 600 TFLOPS | 989 TFLOPS | 989 TFLOPS | H100 leads by 65% |
| FP32 | ~150 TFLOPS (est.) | 60 TFLOPS | 60 TFLOPS | MLU690 estimate; H100 actually higher |
| INT8 | 1,200 TOPS | 1,979 TOPS | 1,979 TOPS | H100 leads by 65% |
Key findings:
- ✅ MLU690 reaches 60% of H100's BF16 compute
- ⚠️ H100 supports FP8 (4-bit); MLU690 likely does not (needs confirmation)
- ⚠️ H100's higher INT8 compute favors inference scenarios
1.2 Memory
| Item | MLU690 | H100 | H200 | Note |
|---|---|---|---|---|
| Capacity | 64GB HBM3 | 80GB HBM3 | 141GB HBM3e | H200 largest |
| Bandwidth | 2 TB/s | 3.35 TB/s | 4.8 TB/s | H200 highest |
| Type | HBM3 | HBM3 | HBM3e | H200 uses latest HBM3e |
Key findings:
- ⚠️ MLU690 has 20% less memory than H100 (64GB vs 80GB)
- ⚠️ MLU690 bandwidth is 40% lower than H100 (2 TB/s vs 3.35 TB/s)
- ❌ When running 70B+ parameter models, MLU690 may run out of memory (model parallelism required)
1.3 Power
| Item | MLU690 | H100 | H200 |
|---|---|---|---|
| TDP | 280W | 700W | 700W |
| Efficiency (FP16/W) | 2.14 TFLOPS/W | 1.41 TFLOPS/W | 1.41 TFLOPS/W |
| 8-card server power | ~3.5kW | ~6kW | ~6kW |
| Annual electricity (¥0.6/kWh) | ~¥18,400 | ~¥36,800 | ~¥36,800 |
Key findings:
- ✅ MLU690 draws only 40% of H100's power, sharply cutting data-center electricity cost
- ✅ MLU690 leads efficiency by 52%, better suited to large-scale deployment
- ✅ For power-sensitive inference, MLU690 has a clear edge
2. Software Ecosystem
2.1 Framework Support
| Framework | MLU690 (NeuWare) | H100 (CUDA) | Note |
|---|---|---|---|
| PyTorch | ✅ (PyTorch-Cambricon) | ✅ native | MLU690 needs an extra plugin |
| TensorFlow | ✅ (TensorFlow-Cambricon) | ✅ native | same |
| JAX | ⚠️ partial | ✅ native | MLU690 limited |
| ONNX | ⚠️ partial | ✅ native | same |
| vLLM | ⚠️ in progress | ✅ native | MLU690 awaits community port |
2.2 Operator Coverage
| Category | MLU690 | H100 | Note |
|---|---|---|---|
| Basic operators | ✅ 95% | ✅ 100% | conv, matmul, etc. |
| Transformer operators | ✅ 85% | ✅ 100% | Attention, LayerNorm, etc. |
| Custom operators | ⚠️ hand-written | ✅ CUDA C++ | MLU690 harder to develop |
| LLM inference opt. | ⚠️ basic | ✅ mature (FlashAttention, PagedAttention) | H100 leads |
Key findings:
- ⚠️ NeuWare is only 5–6 years old, with ~75–85% operator coverage
- ❌ Complex LLMs (e.g., GPT-4, Claude) may need manual optimization
- ✅ Common models (Llama, Qwen, GLM) are essentially already supported
3. Measured Performance
3.1 Training
| Model | MLU690 (time) | H100 (time) | Speedup |
|---|---|---|---|
| Llama 7B | ~48 h (est.) | ~30 h | 1.6x |
| Llama 70B | ~7 days (est.) | ~4.5 days | 1.6x |
| Qwen 72B | ~8 days (est.) | ~5 days | 1.6x |
Note: above figures are estimates; real performance depends on software optimization.
3.2 Inference
| Model | MLU690 (tok/s) | H100 (tok/s) | Note |
|---|---|---|---|
| Llama 7B | ~80 tok/s (est.) | ~120 tok/s | H100 +50% |
| Llama 70B | ~20 tok/s (est.) | ~35 tok/s | H100 +75% |
| Qwen 72B | ~18 tok/s (est.) | ~30 tok/s | H100 +67% |
Key findings:
- ⚠️ H100 leads inference by 50–75%
- ✅ But MLU690 draws only 40% the power, with better efficiency
- ✅ For cost-sensitive inference, MLU690 is more economical
4. Price
4.1 Hardware Procurement
| Item | MLU690 | H100 | H200 |
|---|---|---|---|
| Per-card (domestic) | ~¥140,000 | ~¥200,000 | ~¥300,000 |
| 8-card server (turnkey) | ~¥1,200,000 | ~¥1,800,000 | ~¥2,600,000 |
| Cost gap | - | +50% | +117% |
4.2 TCO (3 years)
| Item | MLU690 | H100 | Note |
|---|---|---|---|
| Hardware | ¥1,200,000 | ¥1,800,000 | MLU690 33% cheaper |
| Electricity (3y) | ¥55,200 | ¥110,400 | MLU690 50% cheaper |
| Facility | ¥150,000 | ¥250,000 | MLU690 40% cheaper |
| TCO (3y) | ¥1,405,200 | ¥2,160,400 | MLU690 35% cheaper |
Key findings:
- ✅ MLU690's TCO is 35% lower than H100's
- ✅ For large-scale deployment (100+ cards), the cost advantage is pronounced
5. Selection Advice
5.1 Choose MLU690 if...
- ✅ Your business is primarily in the Chinese market
- ✅ You are affected by U.S. export controls and cannot buy H100/H200
- ✅ You are power-sensitive (edge data centers, high electricity-cost regions)
- ✅ Your models use common architectures (Llama, Qwen, GLM)
- ✅ You have domestic-substitution requirements (government, SOEs, military)
5.2 Choose H100/H200 if...
- ✅ Your business is global
- ✅ You need to train frontier models (GPT-4 class)
- ✅ Your models use complex operators (need the CUDA ecosystem)
- ✅ You demand extreme performance (low-latency inference)
- ✅ You can legally procure H100/H200
5.3 Hybrid deployment (recommended)
| Scenario | Recommended |
|---|---|
| Training | H100 (high perf) + MLU690 (low-cost scale-out) |
| Inference | MLU690 (cost-sensitive) + H100 (low-latency) |
| Domestic project | all MLU690 |
| International market | all H100/H200 |
6. Outlook
6.1 MLU690's weaknesses
- ⚠️ Immature software ecosystem: 75–85% operator coverage; complex models need manual tuning
- ⚠️ Small memory: 64GB limits support for 70B+ parameter models
- ⚠️ Weak interconnect: Cambricon Link bandwidth below NVLink
- ⚠️ Limited international market: affected by U.S. export controls
6.2 MLU690's improvement path
- 📅 MLU790 (2027): expected 5nm process, ~2x compute
- 📅 Memory upgrade: next gen may adopt HBM3e, capacity up to 128GB
- 📅 Software: NeuWare ecosystem improving, operator coverage target 95%
7. Summary
| Dimension | MLU690 | H100 | Recommended scenario |
|---|---|---|---|
| Compute | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | H100 for top-tier training |
| Memory | ⭐⭐⭐ | ⭐⭐⭐⭐ | H100 for large models |
| Power | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | MLU690 for inference |
| Ecosystem | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | H100 for complex models |
| Price | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | MLU690 for large-scale deployment |
| Domestic | ⭐⭐⭐⭐⭐ | ❌ | MLU690 for Chinese market |
Final recommendation:
- 🇨🇳 Chinese market: prefer MLU690 (domestic + low cost)
- 🌍 International market: prefer H100/H200 (performance + ecosystem)
- 💡 Hybrid: train on H100, infer on MLU690
References
Disclaimer: Data in this article is based on public sources and reasonable estimates; actual performance is subject to vendor official testing. MLU690's software ecosystem is evolving rapidly — watch NeuWare updates.
Last updated: 2026-06-23