Domestic AI Chips vs International AI Chips: 2026 Year-End Comprehensive Comparison - Article Plan
Article status: In planning Planned release: End of December 2026 Last updated: 2026-06-28 Article positioning: Year-end flagship comparison analysis article, comprehensive comparison
Article Outline
I. Introduction (1,500 words)
Core content:
- Overview of the 2026 AI chip market landscape
- Impact of US export controls on the Chinese market
- Background and significance of the rise of domestic chips
- Explanation of the comparison dimensions used in this article
Key data points:
- 2026 global AI chip market size
- China AI chip market size (domestic share)
- Estimated shipment volumes of major vendors
II. Domestic AI Chip Progress (4,000 words)
2.1 Huawei Ascend series
- Ascend 910C: specs, performance, ecosystem, market performance
- Ascend 910B: positioning, use cases
- Progress highlights: successful DeepSeek V4 Pro training case
2.2 Cambricon MLU series
- MLU690: detailed specs, performance benchmarked against the H100
- MLU370/590: positioning, use cases
- Progress highlights: orders from ByteDance, Alibaba, and Baidu
2.3 Other domestic chip vendors
- Enflame: Yunsui T20/T21
- Kunlun: R200/R300 (Baidu in-house)
- Moore Threads: MTT S5000
- T-Head: Hanguang 800 (inference)
- Iluvatar CoreX: Tiangai 150, Zhikai 100 (if ByteDance procurement is confirmed)
- TsingMicro, Hanbo Semiconductor, etc.
Data to collect for this section:
- Detailed specs of each chip (compute, memory, power)
- 2026 shipment data
- List of major customers
- Actual deployment case studies
III. International AI Chip Progress (3,000 words)
3.1 NVIDIA
- H100/H200: current mainstream training cards
- B200/GB200: Blackwell architecture, mass production in 2026
- L40S/L4: inference-only cards
- Progress highlights: B200 performance gains, supply situation
3.2 AMD
- MI300X: benchmarked against the H100
- MI325X: upgraded version
- Progress highlights: growing market share
3.3 Intel
- Gaudi 3: training + inference
- Progress highlights: ecosystem building
3.4 Google
- TPU v5e/v5p: cloud training
- TPU v6 (Trillium): launching in 2026
- Progress highlights: Google Cloud exclusive
3.5 Others
- Cerebras: WSE-3 (full systems)
- SambaNova: SN40L
- Graphcore: Bow IPU (has exited the Chinese market)
Data to collect for this section:
- B200/GB200 detailed specs and performance data
- MI300X/MI325X actual performance tests
- TPU v6 specs
- Supply situation of international chips in the Chinese market
IV. Key Metric Comparison (3,500 words)
4.1 Compute comparison table
| Chip | FP32 | FP16 | INT8 | FP8 | Architecture |
|---|---|---|---|---|---|
| Domestic | |||||
| Ascend 910C | ? | 376 TFLOPS | ? | ? | Da Vinci 3.0 |
| MLU690 | 150 TFLOPS | 600 TFLOPS | 1,200 TOPS | ? | MLUarch 04 |
| Tiangai 150 | ? | ? | ? | Supported | ivcore11 |
| Enflame T21 | ? | ? | ? | ? | DTU 2.0 |
| International | |||||
| H100 SXM | 60 TFLOPS | 989 TFLOPS | 1,979 TOPS | 3,958 TOPS | Hopper |
| H200 SXM | 60 TFLOPS | 989 TFLOPS | 1,979 TOPS | 3,958 TOPS | Hopper |
| B200 | 80 TFLOPS | 2,250 TFLOPS | 4,500 TOPS | 9,000 TOPS | Blackwell |
| MI300X | 163 TFLOPS | 1,271 TFLOPS | 2,542 TOPS | ? | CDNA 3 |
Notes:
- "?" means data to be filled in
- Some FP8 data comes from the web and needs verification
4.2 Memory comparison table
| Chip | Memory Capacity | Memory Type | Memory Bandwidth |
|---|---|---|---|
| Ascend 910C | 64 GB | HBM2E | 2 TB/s (estimated) |
| MLU690 | 64 GB | HBM3 | 2 TB/s (estimated) |
| H100 SXM | 80 GB | HBM3 | 3.35 TB/s |
| H200 SXM | 141 GB | HBM3e | 4.8 TB/s |
| B200 | 192 GB | HBM3e | 8 TB/s |
4.3 Power comparison table
| Chip | TDP | Efficiency (FP16/W) |
|---|---|---|
| Ascend 910C | 310W | 1.21 TFLOPS/W |
| MLU690 | 280W | 2.14 TFLOPS/W |
| H100 SXM | 700W | 1.41 TFLOPS/W |
| B200 | 1000W | 2.25 TFLOPS/W |
4.4 Process node comparison table
| Vendor | Process | Transistor Count (estimated) |
|---|---|---|
| Huawei/Cambricon | 7nm (TSMC) | ~50 billion |
| NVIDIA H100/H200 | 4nm (TSMC) | 80 billion |
| NVIDIA B200 | 4nm (TSMC) | 208 billion |
| AMD MI300X | 5nm (TSMC) | 153 billion |
4.5 Price comparison table
| Chip | International Price (USD) | Domestic Price (CNY) | Notes |
|---|---|---|---|
| H100 | $30,000-$40,000 | - | Subject to export controls |
| H200 | $40,000-$50,000 | - | Subject to export controls |
| B200 | $50,000-$60,000 | - | Subject to export controls |
| Ascend 910C | - | ¥150,000-¥200,000 (estimated) | Domestic sales only |
| MLU690 | - | ¥120,000-¥150,000 (estimated) | Domestic sales only |
Data to collect for this section:
- Ascend 910C detailed compute data (not officially published)
- Tiangai 150 detailed compute data
- Enflame T21 detailed specs
- B200 actual performance test data
- Actual domestic chip prices (vendor quotes vary widely)
V. Ecosystem Comparison (2,500 words)
5.1 Software stack maturity
| Vendor | Software Stack | Framework Support | Operator Coverage | Migration Difficulty |
|---|---|---|---|---|
| NVIDIA | CUDA | Full support | ~100% | - |
| AMD | ROCm | Mainstream support | ~90% | Medium |
| Huawei | CANN | Mainstream support | ~80% | Medium |
| Cambricon | NeuWare | Mainstream support | ~75-85% | Medium |
| Iluvatar CoreX | IXUCA | Mainstream support | ~70% | Low (CUDA-compatible) |
| Enflame | DTU Toolkit | Partial support | ~60% | High |
5.2 Model adaptation status
Adaptation status of mainstream large models:
| Model | H100 | 910C | MLU690 | Tiangai 150 | Notes |
|---|---|---|---|---|---|
| Llama 3 (70B) | ✅ Native | ✅ Adapted | ✅ Adapted | ⚠️ Partial | |
| Qwen2.5 (72B) | ✅ Native | ✅ Adapted | ✅ Adapted | ⚠️ Partial | |
| DeepSeek V3 | ✅ Native | ✅ Training-verified | ⚠️ Inference | ❌ Not supported | |
| GLM-4 (9B) | ✅ Native | ✅ Adapted | ✅ Adapted | ✅ Adapted | |
| Baichuan 2 (13B) | ✅ Native | ✅ Adapted | ✅ Adapted | ✅ Adapted |
5.3 Developer friendliness
- Documentation quality
- Community activity
- Technical support
- Learning curve
Data to collect for this section:
- Latest versions and features of each software stack
- Actual model migration cases and timelines
- Developer community activity data (GitHub stars, issues, etc.)
VI. Market Performance (2,000 words)
6.1 Shipment comparison (2026 estimates)
| Vendor | 2026 Shipments (10k units) | YoY Growth | Major Customers |
|---|---|---|---|
| NVIDIA (global) | 150-200 | +30% | Global tech giants |
| Huawei (Ascend) | 20-30 | +100% | Governments, SOEs, tech companies |
| Cambricon (MLU) | 10-15 | +150% | ByteDance, Alibaba, Baidu |
| AMD (global) | 30-40 | +80% | Meta, Microsoft, etc. |
| Other domestic | 10-20 | +200% | Various AI companies |
6.2 Market share (China market)
| Type | 2024 | 2026 (est.) | Trend |
|---|---|---|---|
| International chips (NVIDIA/AMD) | ~80% | ~40% | ⬇️ Sharp decline |
| Domestic chips (Huawei/Cambricon etc.) | ~20% | ~60% | ⬆️ Rapid rise |
6.3 Major customer distribution
Major domestic chip customers:
- Tech companies: Ascend 910C, MLU690, Tiangai 150
- Tech companies: Ascend 910C, MLU690
- Tencent: Ascend 910C, exploring other domestic chips
- Government/SOEs: Ascend 910C (localization requirements)
- Intelligent computing centers: mixed deployment of multiple domestic chips
Data to collect for this section:
- Q1-Q3 2026 shipment data by vendor
- Major customer procurement announcements
- Intelligent computing center deployment cases
VII. Future Outlook (1,500 words)
7.1 2027 technology roadmap
Domestic chips:
- Huawei: Ascend 920 (5nm, 2027-Q2)
- Cambricon: MLU790 (5nm, 2027-Q4)
- Enflame: Yunsui T30 (5nm, 2027)
- Others: continued monitoring
International chips:
- NVIDIA: X100 (3nm, end of 2027)
- AMD: MI400 (3nm, 2027)
- Intel: Gaudi 4 (5nm, 2027)
7.2 Technology gap forecast
| Dimension | 2026 Gap | 2027 Forecast | 2030 Target |
|---|---|---|---|
| Compute | 40-50% behind | 30-40% behind | Catch up |
| Memory | 20-30% behind | 10-20% behind | Catch up |
| Process | 1-2 generations behind | 1 generation behind | Catch up |
| Ecosystem | 3-5 years behind | 2-3 years behind | Narrow to 1-2 years |
7.3 Market trend forecast
- Continued rise in localization rate: reaching 70% by 2027
- Export controls persist: the US may tighten further
- Accelerated technology iteration: domestic chips moving from "usable" to "good to use"
- Stronger ecosystem building: vendors increasing software investment
VIII. Conclusion (1,000 words)
Core viewpoints:
- Domestic AI chips made significant progress in 2026, with the gap narrowing from "generational" to "one generation"
- Substitution capability is already in place for specific scenarios (inference, localization projects)
- The ecosystem gap remains clear and requires sustained investment
- The market landscape is being reshaped, with the localization rate rising rapidly
- The next 3-5 years are the critical window for domestic chips
Recommendations for different roles:
- Enterprise procurement: how to choose (domestic vs international)
- Developers: which ecosystem to learn
- Investors: which vendors to watch
- Policymakers: how to support domestic chip development
Data Collection Plan
Phase 1: Now - September 2026 (research period)
Weekly tasks
Week 1-2 (2026-06-28 ~ 2026-07-11):
- Create the article outline
- Collect detailed specs of domestic chips (Ascend 910C, MLU690, Tiangai 150, Enflame T21)
- Collect the latest material on international chips (B200/GB200, MI300X/MI325X, TPU v6)
Week 3-4 (2026-07-12 ~ 2026-07-25):
- Create draft comparison tables for each chip
- Collect software stack and ecosystem comparison material
- Search for H1 2026 shipment and market share data
Week 5-8 (2026-07-26 ~ 2026-08-22):
- Collect major customer procurement cases
- Collect actual deployment performance test data
- Follow vendor product launches and technical announcements
Week 9-12 (2026-08-23 ~ 2026-09-30):
- Organize all collected material
- Create charts and data visualizations
- Complete the article first draft (80% of content)
Phase 2: October - November 2026 (data update period)
- Update each chip's latest data (new releases, spec updates)
- Collect Q1-Q3 2026 shipment data
- Follow vendors' promotions and pricing strategies during Double 11/Double 12
- Complete the article first draft (100% of content)
Phase 3: December 2026 (finalization and release period)
- Final data verification and updates
- Article polishing and proofreading
- Create accompanying data visualizations (charts, comparison tables)
- Publish the article
Data Source Checklist
Official sources
- Huawei Ascend official site: https://www.hiascend.com/
- Cambricon official site: https://www.cambricon.com/
- NVIDIA official site: https://www.nvidia.com/
- AMD official site: https://www.amd.com/
- Vendor developer communities and documentation centers
Industry reports
- IDC China AI Chip Market Report (2026)
- Gartner AI Chip Hype Cycle (2026)
- Brokerage research reports (CICC, Huatai, CITIC, etc.)
Technical communities
- Vendors' GitHub open-source projects
- Zhihu technical articles and discussions
- CSDN technical blogs
- Vendor developer forums
News and media
- Reuters, Bloomberg (international market news)
- Caixin, Yicai (domestic market news)
- TechCrunch, The Information (tech news)
Article Supporting Assets
Charts to create
- Compute comparison radar chart: FP32, FP16, INT8, memory bandwidth, energy efficiency
- Memory capacity comparison bar chart: memory capacity of each chip
- Power comparison bar chart: TDP of each chip
- Market share pie chart: 2024 vs 2026 (estimated)
- Technology roadmap timeline: 2024-2027 new product release plans by vendor
- Ecosystem maturity score radar chart: software stack, framework support, operator coverage, community activity
Interactive elements (if the site supports them)
- Filterable comparison table (sortable by compute, power, price, etc.)
- Chip detail cards (hover to show details)
- Interactive ecosystem comparison chart
Publishing and Promotion Plan
Publishing channels
- MirrorFrog website: blog post + comparison table page
- Zhihu: simultaneous publication (with backlinks)
- CSDN: technical article publication
- WeChat official account: if available
- Various tech communities: V2EX, Reddit (r/MachineLearning), etc.
Promotion timeline
- Official announcement: around 2026-12-20
- Social media promotion: within 1 week of publication
- Tech community sharing: within 2 weeks of publication
Risks and Countermeasures
Risk 1: Key data unavailable
Risk: some chips (e.g. Ascend 910C) have no officially published detailed specs
Countermeasures:
- Use third-party test data
- Use estimated values (marked "estimated")
- Contact vendors for materials
Risk 2: Inaccurate market data
Risk: shipment and market share figures are estimates and may be inaccurate
Countermeasures:
- Cross-validate with multiple sources
- Note data sources and confidence levels
- Use range estimates (e.g. 200k-300k units)
Risk 3: Article too long
Risk: the article may exceed 15,000 words, hurting readability
Countermeasures:
- Split into a series (Part 1/2/3)
- Create a standalone interactive comparison table page
- Offer a PDF download version
Next Steps
Immediate (this week)
- Create
docs/research/2026-year-end-comparison-outline.md(this document) - Create
docs/research/data-collection-tracker.xlsx(data collection tracker) - Set up monthly reminders (automated tasks): check vendor product launches and technical updates
Ongoing (monthly)
- Update each chip's latest data
- Collect new test reports and case studies
- Follow industry news and developments
Before year-end
- Complete the article (by 2026-11-30)
- Complete chart creation (by 2026-12-10)
- Complete proofreading and publication (by 2026-12-20)
Article goals:
- ✅ Become the most comprehensive AI chip comparison article of late 2026
- ✅ Provide readers with an objective, well-substantiated decision reference
- ✅ Boost MirrorFrog's authority and traffic
- ✅ Lay the groundwork for the 2027 article series
Success metrics:
- Article length: 10,000-15,000 words
- Chips compared: ≥15
- Data tables: ≥10
- Charts: ≥6
- Expected readership: 5,000+ (website + external platforms)