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China's Domestic AI Chip Triopoly (2026): Ascend, Cambricon, Moore Threads — Who Is the "China H100"?

· 7 min read
Charles Qing
AI Hardware Analyst

Against the backdrop of U.S. export controls, China's AI chip market is forming a "three-way standoff." This article compares the technical routes, product specs, software ecosystems, and commercial progress of the three major domestic AI chip vendors: Huawei Ascend, Cambricon MLU, and Moore Threads MTT.

Key Points​

  • Huawei Ascend: leader in domestic AI training chips; Ascend 950 in mass production; most mature software ecosystem
  • Cambricon MLU690: the "China H100," compute close to H200, clear efficiency advantage
  • Moore Threads MTT S5000: full-function GPU route; achieved Day-0 support for Qwen3.5 and GLM-5.2 in June 2026
  • Shared challenge: affected by U.S. export controls, primarily aimed at the Chinese market, limited internationally

I. Vendor Overview​

VendorFoundedFounderListed2025 RevenueMain Customers
Huawei Ascend2018 (division)Ren Zhengfeiprivate (wholly owned by Huawei)~¥20B (est.)Chinese gov, SOEs, military
Cambricon2016Chen Tianshi (CAS)2020-07 (STAR Market 688256)~¥5.2BByteDance, Alibaba, Baidu
Moore Threads2020Zhang Jianzhong (ex-NVIDIA China)2023-12 (STAR Market 688495)~¥1.5B (est.)gov, SOEs, gaming cos.

Strategic Positioning​

VendorTech routeCore strengthMain challenge
Huawei AscendAI-training-specific (Da Vinci)co-optimized HW/SW, carrier channelssanctions, process limits
CambriconAI-training-specific (MLUarch)high efficiency, competitive priceimmature ecosystem
Moore ThreadsFull-function GPU (MUSA)graphics + AI + general compute, Day-0 supportcompute below dedicated AI chips

II. Flagship Product Comparison​

1. Huawei Ascend 950DT (2026 flagship)​

ItemSpec
BF16 compute1,000 TFLOPS
Memory144GB HiZQ 2.0 (in-house HBM)
Memory bandwidth4 TB/s
TDP400W
ProcessN+2 (improved 7nm)
Released2026-04
Mass production2026-Q2
Unit price~¥80,000 (est.)

Strengths:

  • ✅ High large-model inference throughput: 144GB memory friendly to DeepSeek R1 (671B MoE)
  • ✅ Most mature ecosystem: CANN ~85% operator coverage, supports PyTorch, TensorFlow
  • ✅ Strong carrier channel: China Mobile, China Telecom large purchases

Weaknesses:

  • ❌ Process limited: N+2 below TSMC 4nm
  • ❌ Mediocre efficiency: 400W TDP, 2.5 TFLOPS/W

2. Cambricon MLU690 (2026 flagship)​

ItemSpec
BF16 compute600 TFLOPS
Memory64GB HBM3
Memory bandwidth2 TB/s
TDP280W
ProcessTSMC 7nm
Released2025-Q4
Mass production2026-Q1
Unit price~¥140,000 (est.)

Strengths:

  • ✅ Best efficiency: 280W TDP, 2.14 TFLOPS/W (1.5x H100)
  • ✅ Competitive price: ~$20,000, 33% cheaper than H100
  • ✅ Top-tier customer orders: ByteDance, Alibaba, Baidu

Weaknesses:

  • ❌ Small memory: 64GB limits large-model training scale
  • ❌ Immature ecosystem: NeuWare ~75–85% coverage; complex LLMs need manual tuning

3. Moore Threads MTT S5000 (2025 flagship)​

ItemSpec
FP16 compute~1,000 TFLOPS (est.)
Memory80GB GDDR6X
Memory bandwidth1.6 TB/s
TDP~350W
ProcessTSMC 4nm (est.)
Released2025-02
Mass production2025-Q2
Unit price~¥50,000 (est.)

Strengths:

  • ✅ Full-function GPU: graphics + AI + general compute, broader scenarios
  • ✅ Strong Day-0 support: June 2026 Day-0 support for Qwen3.5, GLM-5.2, MiniMax M3
  • ✅ Lowest price: ~¥50,000, high cost-performance

Weaknesses:

  • ❌ Compute below dedicated AI chips: FP16 ~50% of H100
  • ❌ Low memory bandwidth: 1.6 TB/s (48% of H100), limits large-model training

III. Compute Comparison (BF16/FP16)​

ChipBF16 computeMemoryBandwidthTDPEfficiency
Huawei Ascend 950DT1,000 TFLOPS144GB4 TB/s400W2.5 TFLOPS/W
Cambricon MLU690600 TFLOPS64GB2 TB/s280W2.14 TFLOPS/W
Moore Threads MTT S5000~1,000 TFLOPS80GB1.6 TB/s~350W~2.86 TFLOPS/W
NVIDIA H100989 TFLOPS80GB3.35 TB/s700W1.41 TFLOPS/W
NVIDIA H200989 TFLOPS141GB4.8 TB/s700W1.41 TFLOPS/W

Key insights:

  1. Ascend 950DT has the highest compute (1,000 TFLOPS) but mediocre efficiency
  2. Cambricon MLU690 has the best efficiency (2.14 TFLOPS/W), TDP only 280W
  3. Moore Threads MTT S5000 wins on full-function versatility but low bandwidth

IV. Software Ecosystem​

VendorStackFramework supportCoverageMaturity
Huawei AscendCANNPyTorch, TensorFlow, MindSpore~85%⭐⭐⭐⭐ (4/5)
CambriconNeuWarePyTorch-Cambricon, TensorFlow-Cambricon~75–85%⭐⭐⭐ (3/5)
Moore ThreadsMUSIFYPyTorch, TensorFlow, ONNX~70%⭐⭐⭐ (3/5)
NVIDIACUDAall~99%⭐⭐⭐⭐⭐ (5/5)

Ecosystem Maturity Assessment​

Huawei Ascend CANN:

  • ✅ Strength: highest operator coverage, supports MindSpore (in-house framework)
  • ❌ Weakness: steep learning curve, incomplete docs

Cambricon NeuWare:

  • ✅ Strength: PyTorch/TensorFlow compatible, low migration cost
  • ❌ Weakness: complex LLMs need manual tuning

Moore Threads MUSIFY:

  • ✅ Strength: strong Day-0 support, ONNX support
  • ❌ Weakness: lowest operator coverage, dual graphics+AI engine complexity

V. Commercial Progress​

Vendor2026 commercial progressMain customersShipments
Huawei AscendAscend 950 mass production; China Mobile large purchaseChina Mobile, China Telecom, gov~100K/yr (est.)
CambriconMLU690 mass production; ByteDance, Alibaba ordersByteDance, Alibaba, Baidu~50K/yr (est.)
Moore ThreadsMTT S5000 mass production; Day-0 Qwen3.5gov, SOEs, gaming cos.~30K/yr (est.)

Latest as of June 2026​

Huawei Ascend:

  • ✅ Ascend 950DT fully ramping
  • ✅ ¥1B procurement agreement with China Mobile

Cambricon:

  • ✅ MLU690 in volume shipment
  • ✅ ByteDance order ~20K units

Moore Threads:

  • ✅ Day-0 support for Qwen3.5, GLM-5.2, MiniMax M3
  • ✅ MTT S5000 2nd-gen released

VI. Selection Advice​

Scenario 1: Trillion-parameter training (GPT-4 class)​

Recommended: Huawei Ascend 950DT

  • ✅ 144GB large memory supports super-large models
  • ✅ Most mature ecosystem (~85% coverage)
  • ✅ Strong carrier channel, Chinese government backing

Alternative: Cambricon MLU690 (high efficiency, but small memory)

Scenario 2: Tens-to-hundreds-of-billions parameter training​

Recommended: Cambricon MLU690

  • ✅ Best efficiency (2.14 TFLOPS/W), low TCO
  • ✅ Competitive price (~$20,000)
  • ✅ Validated by top customers (ByteDance, Alibaba)

Alternative: Huawei Ascend 920 (more compute, mediocre efficiency)

Scenario 3: Cloud AI inference​

Recommended: Huawei Ascend 950PR (inference-specific)

  • ✅ Well-optimized inference throughput
  • ✅ 128GB memory friendly to MoE models
  • ✅ Mature stack, low deployment cost

Alternative: Moore Threads MTT S5000 (full-function GPU, inference + graphics)

Scenario 4: Edge AI / on-device inference​

Recommended: Moore Threads MTT S5000

  • ✅ Full-function GPU, graphics + AI
  • ✅ Lowest price (~¥50,000)
  • ✅ Strong Day-0 support

Alternative: Huawei Ascend 310 (low power, 8W TDP)

Scenario 5: Domestic substitution (gov, SOEs)​

Recommended: Huawei Ascend 950DT

  • ✅ Chinese government first choice, carrier bulk buys
  • ✅ Co-optimized HW/SW, stable performance
  • ✅ Supported by national semiconductor fund

Alternative: Cambricon MLU690 (high efficiency, competitive price)

VII. Future Roadmap​

Vendor2026 H220272028
Huawei Ascend950DT ramp960 (FP8 ~2 PFLOPS)970 (N+3 process)
CambriconMLU690 rampMLU790 (5nm, BF16 ~1,000 TFLOPS)MLU890 (3nm)
Moore ThreadsMTT S5000 2nd-genMTT S6000 (HBM3, FP16 ~1,500 TFLOPS)MTT S7000

VIII. Summary: Who Is the "China H100"?​

DimensionAscend 950DTMLU690MTT S5000
Compute⭐⭐⭐⭐⭐ (5/5)⭐⭐⭐ (3/5)⭐⭐⭐ (3/5)
Memory⭐⭐⭐⭐⭐ (5/5)⭐⭐ (2/5)⭐⭐⭐ (3/5)
Efficiency⭐⭐⭐ (3/5)⭐⭐⭐⭐⭐ (5/5)⭐⭐⭐⭐ (4/5)
Ecosystem⭐⭐⭐⭐ (4/5)⭐⭐⭐ (3/5)⭐⭐⭐ (3/5)
Price⭐⭐⭐ (3/5)⭐⭐⭐⭐ (4/5)⭐⭐⭐⭐⭐ (5/5)
Overall⭐⭐⭐⭐ (4/5)⭐⭐⭐ (3/5)⭐⭐⭐ (3/5)

Final conclusion:

  • Huawei Ascend 950DT is the domestic AI training chip closest to H100, strongest overall
  • Cambricon MLU690 is the most efficient domestic AI chip, lowest TCO
  • Moore Threads MTT S5000 is the cheapest full-function GPU, suited to edge AI and graphics+AI

References​

Disclaimer: Data based on public sources; actual specs per vendor official. MirrorFrog continuously updates domestic AI chip data — corrections welcome.

Changelog: 2026-06-23 initial release