15 posts tagged with "Domestic Chips"

Chinese domestic AI chip development

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

Computex 2026 Wrap-Up: AI PC Chip War Begins, NVIDIA RTX Spark Arrives Fall 2026

June 6, 2026 · 3 min read

AI Compute Cards Wiki Editorial

Industry Research Team

June 6, 2026 — COMPUTEX 2026 concluded yesterday in Taipei. Under the theme "AI Together," this year's event set records with 1,500+ exhibitors and 6,000 booths. The head-to-head battle between NVIDIA, Intel, and AMD in the AI PC space was the defining story of the show.

1. NVIDIA RTX Spark: June Launch at $1,399

Less than a week after its COMPUTEX debut, the NVIDIA-MediaTek RTX Spark Superchip confirmed its commercial timeline:

Detail	Info
Launch OEMs	ASUS, Dell, HP, Lenovo, Microsoft Surface, MSI
Availability	Fall 2026
Starting Price	Not yet announced (analysts estimate $3,000-4,000)
Core Specs	Arm CPU (up to 20 cores) + Blackwell GPU (6,144 CUDA cores)
Unified Memory	128 GB LPDDR5X (300 GB/s)
Model Capacity	Runs 120B parameter models, up to 1M token context

Market Reaction: AMD, Intel, and Qualcomm shares fell following the announcement. Analysts believe RTX Spark will reshape the market across three fronts — Windows AI PCs, creator workstations, and edge inference nodes.

2. Intel 18A in Full Production: Clearwater Forest + Crescent Island

Intel CEO Lip-Bu Tan delivered his first COMPUTEX keynote with two key updates:

Clearwater Forest (Xeon 6+)

288 cores, Darkmont architecture
First Intel 18A process node data center CPU
Foveros Direct 3D packaging
Now in full production

Crescent Island AI GPU

480 GB LPDDR5x memory
350 W air-cooled PCIe form factor
Native FP4 support, targeting agentic inference
Shipping H2 2026

"As AI moves into the agentic era, the CPU returns to the center of modern AI infrastructure." — Lip-Bu Tan

3. AMD Ryzen AI 400 Series Now Shipping

AMD showcased the Ryzen AI 400 series (Zen 5 + Zen 5C hybrid + XDNA2 NPU) at COMPUTEX:

NPU performance: 60 TOPS, the highest in x86
7 consumer SKUs + commercial PRO series
Multiple OEM models already available or launching soon
Advancing AI 2026 summit set for July in San Francisco

4. Chinese Domestic Chips Gaining Momentum

Vendor	Product	Status
Huawei	Ascend 950PR/950DT	In production, self-developed HBM
Cambricon	MLU690	2 PFLOPS FP8, shipping
Moore Threads	MTT S5000	1,000 TFLOPS, specs public

5. The AI PC Era: Three-Way Roadmap Comparison

Dimension	NVIDIA RTX Spark	Intel Clearwater Forest + Crescent Island	AMD Ryzen AI 400
CPU Cores	20-core Grace (Arm)	288-core Darkmont (x86)	Up to 12-core Zen5+5C
GPU/NPU	Blackwell GPU	Crescent Island (discrete GPU)	XDNA2 NPU (60 TOPS)
AI Compute	1 PFLOPS	TBD	60 TOPS NPU
Target	Personal AI agents	Dual-track: DC + AI PC	Copilot+ PC
Process	TSMC 4NP	Intel 18A	TSMC 4nm
Availability	June 2026	H2 2026	Shipping now

This Week in AI Compute (6/1 – 6/6)

Date	Event
Jun 1	NVIDIA GTC Taipei: RTX Spark, Vera Rubin production, DGX Station for Windows
Jun 1	Intel unveils Crescent Island, Clearwater Forest
Jun 2	COMPUTEX 2026 opens: "AI Together"
Jun 5	COMPUTEX closes: 1,500+ exhibitors, record scale
Jun 6	RTX Spark confirmed June launch at $1,399

Sources: COMPUTEX Daily, Tencent News, Phoenix Technology, Xueqiu, The Silicon Review.

Huawei Ascend 950 Mass Production and the Full Picture of China's AI Chip Ecosystem

June 4, 2026 · 4 min read

AI Compute Cards Wiki Editorial

Industry Research Team

June 2026 — Huawei's Ascend 950 series (950PR / 950DT) has entered formal mass production and delivery, a landmark event for China's AI chip industry in 2026. Meanwhile, Cambricon's MLU690 has begun shipping and Moore Threads has announced MTT S5000 specifications, formally establishing China's tri-polar AI chip landscape.

Ascend 950 Series: A Historic Breakthrough with Self-Developed HBM

Huawei HiSilicon's Ascend 950 series is the fourth-generation Ascend AI chip, first revealed at Huawei Connect 2025 in September and entering mass production in Q1 2026.

950PR (Prefill Inference Specialized)

Item	Specification
Architecture	Da Vinci v5 (SIMD + SIMT dual-model)
Process	N+2 (SMIC domestic)
HBM	HiBL 1.0 (Huawei self-developed) , 128 GB
FP8 Compute	1 PFLOPS (HiF8 format)
TDP	~400 W
Target	Inference Prefill (video recommendation, real-time interaction)

950DT (Decode + Training Specialized)

Item	Specification
Architecture	Da Vinci v5 (SIMD + SIMT dual-model)
Process	N+2 (SMIC domestic)
HBM	HiZQ 2.0 (Huawei self-developed) , 144 GB, 4 TB/s
FP8 Compute	1 PFLOPS (HiF8 format)
TDP	~500 W
Target	Inference Decode + Model Training

Historical Significance

Self-developed HBM (HiBL 1.0 / HiZQ 2.0) represents the most important technical breakthrough of Huawei Ascend 950 — this is the first time a Chinese enterprise has achieved self-developed mass production of HBM memory, completely eliminating dependence on SK Hynix / Samsung HBM supply. Combined with the domestic N+2 process, Ascend 950 has achieved full-chain domestic production from HBM → Compute Die → Packaging → System.

Cambricon MLU690: China's Only Native FP8 Support

Cambricon's seventh-generation AI chip MLU 690 (Siyuan 690) began volume production and shipping in H1 2026. This is the first domestic AI chip with native FP8 precision support.

Item	MLU 690
Process	5nm (TSMC / SMIC)
FP8 dense	2 PFLOPS
HBM	192GB HBM3E, 5 TB/s
TDP	~500 W
Unit Price (OAM)	~$8,000-12,000

MLU 690's FP8 compute power (2 PFLOPS dense) is on paper comparable to NVIDIA Blackwell (B200 FP8 4.5 PFLOPS sparse). Leveraging its financing advantage as a STAR Market listed company, Cambricon targets 2026 revenue of ¥15-20B (2025: ¥7.2B).

Moore Threads MTT S5000: From Graphics to Training-Inference Unified

Moore Threads publicly disclosed detailed specifications of the MTT S5000 in February 2026, featuring the fourth-generation MUSA "Pinghu" architecture, single-card AI compute of 1,000 TFLOPS, 80GB GDDR6X memory, 1.6 TB/s bandwidth.

Moore Threads pursues a full-function GPU path (graphics rendering + AI compute + general-purpose compute), closest to NVIDIA's strategy. The founding team comes from former NVIDIA China, and the MUSIFY toolchain helps auto-migrate CUDA code to the MUSA platform, lowering ecosystem migration costs.

China's Tri-Polar AI Chip Landscape

Dimension	Huawei Ascend	Cambricon	Moore Threads
Core Architecture	Da Vinci v5	MLUv07	MUSA 4th Gen
Process	N+2 domestic	5nm	6nm
FP8 Compute	~1 PFLOPS	2 PFLOPS	0.5 PFLOPS (estimated)
HBM Self-Sufficiency	✅ Self-developed HiBL/HiZQ	❌ Purchased	❌ Purchased
Ecosystem	CANN + MindSpore	NeuWare + MindSpore	MUSA + MUSIFY
Advantage	Full-chain domestic	Highest FP8 compute	Full-function + CUDA migration
2025 Revenue	(Huawei internal)	¥7.2B	¥2.2B

Global Market Comparison (Q2 2026 Update)

Tier	Vendor	Flagship Chip	FP8/PFLOPS	HBM	Mass Production
Tier 1	NVIDIA	Rubin R200	25 PF (sparse)	288GB HBM4	2026 H2
Tier 2	AMD	MI400	20 PF (dense)	432GB HBM4	2026
	Huawei	Ascend 950DT	1 PF (dense)	144GB self-developed HBM	2026 Q1
	Cambricon	MLU690	2 PF (dense)	192GB HBM3E	2026 H1
	AWS	Trainium 3	5.7 PF (dense)	144GB HBM	2025 Q4 GA
Tier 3	Intel	Gaudi 3	1.8 PF	128GB HBM2e	In production
	Google	TPU v7	4.6 PF(TFLOPS)	192GB HBM	2025
	Moore Threads	MTT S5000	1 PF	80GB GDDR6X	2025 Q1

Note: NVIDIA uses sparse compute as standard, while AMD / Huawei / Cambricon use dense — not directly comparable.

Outlook for H2 2026

NVIDIA Rubin R200: Official shipment in H2 2026, 288GB HBM4, 6-chip CoWoS-L packaging
Huawei Ascend 960: Roadmap H2 2027, expected FP8 compute doubled to 2 PFLOPS
Cambricon MLU790: Expected 2027, 3nm, 384GB HBM4, 2.5 PFLOPS
Moore Threads: Next-gen GPU expected with HBM3, 2× MTT S5000 compute

By 2026, China's AI chip industry has formed a complete product matrix from Training (Cambricon MLU690 / Ascend 950DT) → Inference (Ascend 950PR / Moore Threads S5000) → Systems (CloudMatrix / Distributed Clusters).

This article is based on public information from Huawei Connect 2025 (2025-09-18), industry analysis reports from April 2026, and the latest market data as of June 2026.

China AI Chip Landscape 2025: Ascend, Cambricon, Hygon — Who Will Dominate?

June 3, 2025 · 5 min read

AI Compute Cards Wiki Editorial

Industry Research Team

Escalating U.S. export controls are forcing China's AI chip industry to accelerate self-reliance. By 2025, the discussion around domestic Chinese AI chips has shifted from "are they usable?" to "which one should I choose?"

This article systematically reviews the major players, core products, and actual deployment status of domestic AI chips, helping developers and procurement decision-makers understand the competitive landscape.

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​

1. NVIDIA RTX Spark: June Launch at $1,399​

2. Intel 18A in Full Production: Clearwater Forest + Crescent Island​

Clearwater Forest (Xeon 6+)​

Crescent Island AI GPU​

3. AMD Ryzen AI 400 Series Now Shipping​

4. Chinese Domestic Chips Gaining Momentum​

5. The AI PC Era: Three-Way Roadmap Comparison​

This Week in AI Compute (6/1 – 6/6)​

Ascend 950 Series: A Historic Breakthrough with Self-Developed HBM​

950PR (Prefill Inference Specialized)​

950DT (Decode + Training Specialized)​

Historical Significance​

Cambricon MLU690: China's Only Native FP8 Support​

Moore Threads MTT S5000: From Graphics to Training-Inference Unified​

China's Tri-Polar AI Chip Landscape​

Global Market Comparison (Q2 2026 Update)​

Outlook for H2 2026​

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

1. NVIDIA RTX Spark: June Launch at $1,399

2. Intel 18A in Full Production: Clearwater Forest + Crescent Island

Clearwater Forest (Xeon 6+)

Crescent Island AI GPU

3. AMD Ryzen AI 400 Series Now Shipping

4. Chinese Domestic Chips Gaining Momentum

5. The AI PC Era: Three-Way Roadmap Comparison

This Week in AI Compute (6/1 – 6/6)

Ascend 950 Series: A Historic Breakthrough with Self-Developed HBM

950PR (Prefill Inference Specialized)

950DT (Decode + Training Specialized)

Historical Significance

Cambricon MLU690: China's Only Native FP8 Support

Moore Threads MTT S5000: From Graphics to Training-Inference Unified

China's Tri-Polar AI Chip Landscape

Global Market Comparison (Q2 2026 Update)

Outlook for H2 2026