2 posts tagged with "MLU690"

寒武纪 MLU690 芯片详解

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

June 23, 2026 · 7 min read

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

Vendor	Founded	Founder	Listed	2025 Revenue	Main Customers
Huawei Ascend	2018 (division)	Ren Zhengfei	private (wholly owned by Huawei)	~¥20B (est.)	Chinese gov, SOEs, military
Cambricon	2016	Chen Tianshi (CAS)	2020-07 (STAR Market 688256)	~¥5.2B	ByteDance, Alibaba, Baidu
Moore Threads	2020	Zhang Jianzhong (ex-NVIDIA China)	2023-12 (STAR Market 688495)	~¥1.5B (est.)	gov, SOEs, gaming cos.

Strategic Positioning

Vendor	Tech route	Core strength	Main challenge
Huawei Ascend	AI-training-specific (Da Vinci)	co-optimized HW/SW, carrier channels	sanctions, process limits
Cambricon	AI-training-specific (MLUarch)	high efficiency, competitive price	immature ecosystem
Moore Threads	Full-function GPU (MUSA)	graphics + AI + general compute, Day-0 support	compute below dedicated AI chips

II. Flagship Product Comparison

1. Huawei Ascend 950DT (2026 flagship)

Item	Spec
BF16 compute	1,000 TFLOPS
Memory	144GB HiZQ 2.0 (in-house HBM)
Memory bandwidth	4 TB/s
TDP	400W
Process	N+2 (improved 7nm)
Released	2026-04
Mass production	2026-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)

Item	Spec
BF16 compute	600 TFLOPS
Memory	64GB HBM3
Memory bandwidth	2 TB/s
TDP	280W
Process	TSMC 7nm
Released	2025-Q4
Mass production	2026-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)

Item	Spec
FP16 compute	~1,000 TFLOPS (est.)
Memory	80GB GDDR6X
Memory bandwidth	1.6 TB/s
TDP	~350W
Process	TSMC 4nm (est.)
Released	2025-02
Mass production	2025-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)

Chip	BF16 compute	Memory	Bandwidth	TDP	Efficiency
Huawei Ascend 950DT	1,000 TFLOPS	144GB	4 TB/s	400W	2.5 TFLOPS/W
Cambricon MLU690	600 TFLOPS	64GB	2 TB/s	280W	2.14 TFLOPS/W
Moore Threads MTT S5000	~1,000 TFLOPS	80GB	1.6 TB/s	~350W	~2.86 TFLOPS/W
NVIDIA H100	989 TFLOPS	80GB	3.35 TB/s	700W	1.41 TFLOPS/W
NVIDIA H200	989 TFLOPS	141GB	4.8 TB/s	700W	1.41 TFLOPS/W

Key insights:

Ascend 950DT has the highest compute (1,000 TFLOPS) but mediocre efficiency

Cambricon MLU690 has the best efficiency (2.14 TFLOPS/W), TDP only 280W

Moore Threads MTT S5000 wins on full-function versatility but low bandwidth

IV. Software Ecosystem

Vendor	Stack	Framework support	Coverage	Maturity
Huawei Ascend	CANN	PyTorch, TensorFlow, MindSpore	~85%	⭐⭐⭐⭐ (4/5)
Cambricon	NeuWare	PyTorch-Cambricon, TensorFlow-Cambricon	~75–85%	⭐⭐⭐ (3/5)
Moore Threads	MUSIFY	PyTorch, TensorFlow, ONNX	~70%	⭐⭐⭐ (3/5)
NVIDIA	CUDA	all	~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

Vendor	2026 commercial progress	Main customers	Shipments
Huawei Ascend	Ascend 950 mass production; China Mobile large purchase	China Mobile, China Telecom, gov	~100K/yr (est.)
Cambricon	MLU690 mass production; ByteDance, Alibaba orders	ByteDance, Alibaba, Baidu	~50K/yr (est.)
Moore Threads	MTT S5000 mass production; Day-0 Qwen3.5	gov, 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

Vendor	2026 H2	2027	2028
Huawei Ascend	950DT ramp	960 (FP8 ~2 PFLOPS)	970 (N+3 process)
Cambricon	MLU690 ramp	MLU790 (5nm, BF16 ~1,000 TFLOPS)	MLU890 (3nm)
Moore Threads	MTT S5000 2nd-gen	MTT S6000 (HBM3, FP16 ~1,500 TFLOPS)	MTT S7000

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

Dimension	Ascend 950DT	MLU690	MTT 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

Huawei Ascend 950 series — MirrorFrog detailed specs
Cambricon MLU690 — MirrorFrog detailed specs
Moore Threads MTT S5000 — MirrorFrog detailed specs
Full AI chip comparison table — 100+ chip specs
2026 H2 AI chip roadmap major update — latest roadmap

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

Cambricon MLU690 vs NVIDIA H100: In-Depth Comparison — Can a Domestic AI Chip Replace the H100?

June 23, 2026 · 6 min read

Charles Qing

AI Hardware Analyst

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

Key Points​

I. Vendor Overview​

Strategic Positioning​

II. Flagship Product Comparison​

1. Huawei Ascend 950DT (2026 flagship)​

2. Cambricon MLU690 (2026 flagship)​

3. Moore Threads MTT S5000 (2025 flagship)​

III. Compute Comparison (BF16/FP16)​

IV. Software Ecosystem​

Ecosystem Maturity Assessment​

V. Commercial Progress​

Latest as of June 2026​

VI. Selection Advice​

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

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

Scenario 3: Cloud AI inference​

Scenario 4: Edge AI / on-device inference​

Scenario 5: Domestic substitution (gov, SOEs)​

VII. Future Roadmap​

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

References​

Core Verdict (Read This First)​

1. Detailed Spec Comparison​

1.1 Compute​

1.2 Memory​

1.3 Power​

2. Software Ecosystem​

2.1 Framework Support​

2.2 Operator Coverage​

3. Measured Performance​

3.1 Training​

3.2 Inference​

4. Price​

4.1 Hardware Procurement​

4.2 TCO (3 years)​

5. Selection Advice​

5.1 Choose MLU690 if...​

5.2 Choose H100/H200 if...​

5.3 Hybrid deployment (recommended)​

6. Outlook​

6.1 MLU690's weaknesses​

6.2 MLU690's improvement path​

7. Summary​

References​

Key Points

I. Vendor Overview

Strategic Positioning

II. Flagship Product Comparison

1. Huawei Ascend 950DT (2026 flagship)

2. Cambricon MLU690 (2026 flagship)

3. Moore Threads MTT S5000 (2025 flagship)

III. Compute Comparison (BF16/FP16)

IV. Software Ecosystem

Ecosystem Maturity Assessment

V. Commercial Progress

Latest as of June 2026

VI. Selection Advice

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

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

Scenario 3: Cloud AI inference

Scenario 4: Edge AI / on-device inference

Scenario 5: Domestic substitution (gov, SOEs)

VII. Future Roadmap

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

References

Core Verdict (Read This First)

1. Detailed Spec Comparison

1.1 Compute

1.2 Memory

1.3 Power

2. Software Ecosystem

2.1 Framework Support

2.2 Operator Coverage

3. Measured Performance

3.1 Training

3.2 Inference

4. Price

4.1 Hardware Procurement

4.2 TCO (3 years)

5. Selection Advice

5.1 Choose MLU690 if...

5.2 Choose H100/H200 if...

5.3 Hybrid deployment (recommended)

6. Outlook

6.1 MLU690's weaknesses

6.2 MLU690's improvement path

7. Summary

References