2026 Domestic AI Chip Progress: Huawei Ascend 950, Baidu Kunlun M100, Alibaba T-Head M890 Fully Explained
· 15 min read
In 2026, China's domestic AI chip industry has entered a period of full-scale explosion. The three giants — Huawei Ascend, Baidu Kunlun, and Alibaba T-Head — have successively launched next-generation products, while Cambricon, MetaX, Enflame, and Iluvatar have also achieved important breakthroughs.
This article comprehensively analyzes 2026 domestic AI chip progress across four dimensions: product launches, technology breakthroughs, market dynamics, and ecosystem building.
1. Huawei Ascend: 950 series launched, 960/970 roadmap clear
1.1 Ascend 950PR (launched Q1 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | March 21, 2026 |
| Platform | Atlas 350 accelerator card |
| HBM capacity | 128 GB (Huawei in-house HiBL 1.0 HBM) |
| Memory bandwidth | 1.6 TB/s |
| FP8 compute | 1 PFLOPS |
| Positioning | Inference-specific (Prefill stage) |
| Performance vs. | Single-card compute is 2.87× NVIDIA H20 |
Technology innovations:
- First adoption of Huawei in-house HBM solution (HiBL 1.0), lowering cost
- Supports FP8 low-precision compute, 3× inference energy-efficiency improvement
- Optimized for inference scenarios such as video recommendation and real-time interaction
Commercialization progress:
- Mass supply began in Q1 2026
- Key customers: China Telecom, China Mobile, China Unicom, Huawei Cloud
- Priced around ¥100,000/card (¥80,000 for key accounts), ~30% lower than comparable competitors
1.2 Ascend 950DT (launched Q4 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q4 2026 (expected October) |
| HBM capacity | 144 GB (Huawei in-house HiZQ 2.0 HBM) |
| Memory bandwidth | 4 TB/s (HiZQ 2.0 technology) |
| FP8 compute | 1 PFLOPS |
| Positioning | Inference + training (Decode stage + training tasks) |
| Technology innovation | First to carry in-house HiZQ 2.0 memory technology |
Technology innovations:
- Adopts HiZQ 2.0 memory technology, 2× data-movement efficiency
- Supports FP8/FP4 low-precision compute, balancing performance and efficiency
- Optimized for scenarios such as dialogue generation and large-model training
1.3 950 SuperNode (launched Q4 2026)
System specs:
| Item | Configuration |
|---|---|
| Max interconnected chips | 8,192 chips |
| Total FP8 compute | 1 EFLOPS (1,024-card scale) |
| 1024-card version | 16 liquid-cooled cabinets, 64 chips per cabinet |
| Supported models | Trillion-parameter large-model training |
| Deployment progress | 1,024-card version already in deployment |
Performance comparison:
- 950 SuperNode outperforms NVIDIA's 2027 NVL576 system
- Leads by 20% in trillion-parameter model training
1.4 Ascend 960/970 roadmap
| Chip model | Launch | Core specs | Positioning |
|---|---|---|---|
| Ascend 960 | Q4 2027 | N+3 process, 288GB HBM, FP8 2 PFLOPS, 30%+ better energy efficiency than 910C | Ultra-large-scale training |
| Ascend 970 | Q4 2028 | N+3 process, FP4 8 PFLOPS, 4 TB/s bandwidth, supports trillion-parameter models | Next-gen AI architecture (MoE, etc.) |
Technology breakthroughs:
- Process upgrade: from N+2 (7nm-class) to N+3 (5nm-class)
- Memory capacity doubled: from 144GB (950DT) to 288GB (960/970)
- Energy efficiency improved: 960/970 are 30%+ better than 910C
- Precision optimized: 970 supports FP4 precision, optimized for next-gen AI architectures (MoE, etc.)
1.5 Commercialization progress
Shipment data:
- 384-card SuperNode: over 500 units deployed, the only truly large-scale commercial SuperNode in China
- 2026 shipment target: 800k chips (1M cumulative)
- Market share: 60% of China's AI chip market
Ecosystem building:
- CANN compiler: open-sourced end of 2025, seamless PyTorch/TensorFlow migration
- Mind series toolchains: fully open, lowering the developer barrier
- Ecosystem partners: over 3,000
- Developer community: over 500k registered developers
2. Baidu Kunlun: M100 inference-specific, Tianchi SuperNode deployed
2.1 Kunlun M100 (launched early 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Early 2026 (expected Q2) |
| Positioning | Inference-specific |
| Architecture | In-house XPU-P architecture (inference-optimized) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 64 GB (inference-optimized) |
| TDP | 250 W (low-power inference) |
| Performance vs. | 1.5× P800 inference, 38% lower power |
Technology innovations:
- Adopts RISC-V open instruction set, adding 50+ AI-specific instructions
- Compute per watt reaches 8.3 TOPS/W, 2.1× the industry average
- Supports models from 10 billion to 100 billion parameters for inference
Commercialization progress:
- Mass supply in Q2 2026
- Key customers: Baidu Smart Cloud, China Merchants Bank, Southern Grid, Geely Auto
- Priced around ¥60,000/card, clear cost-performance advantage
2.2 Kunlun M300 (launched early 2027)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q1 2027 (expected March) |
| Positioning | Ultra-large-scale multimodal training |
| Architecture | In-house XPU-P architecture (multimodal-optimized) |
| Process | 5nm (SMIC N+3) |
| HBM capacity | 256 GB HBM4 |
| TDP | 500 W |
| Modalities | Text, images, video and other data types |
Technology innovations:
- Adopts HBM4 memory, bandwidth up to 3.2 TB/s
- Supports FP8/FP4 low-precision compute, 2× training energy-efficiency
- Native support for multimodal model training (text + image + video)
2.3 Tianchi 256-card SuperNode (launched June 2026)
System specs:
| Item | Configuration |
|---|---|
| Launch date | June 2026 (expected) |
| Chip count | 256 Kunlun P800/M100 |
| Effective training rate | 97% |
| Inter-chip bandwidth | 1.2 TB/s |
| Validated models | Baidu ERNIE 5.1 and other key large models |
Performance breakthroughs:
- Fully domestic SuperNode, fully autonomous and controllable from chip to network
- 97% effective training rate, surpassing NVIDIA DGX SuperPOD's 95%
- Training validation completed for Baidu ERNIE 5.1 and other key large models
2.4 Commercialization progress
Shipment data:
- P800: 150k shipped in 2025, 200k target in 2026
- Ten-thousand-card clusters: multiple P800-based clusters delivered
- Market share: 20% of China's AI chip market
Customer coverage:
- External customer revenue share: over 50% in 2025
- China Mobile AI server procurement: P800-based bids won 70%, 70%, 100% shares
- Key customers: China Merchants Bank, Southern Grid, Geely Auto, iFlytek
IPO progress:
- May 2026: officially launched STAR Market IPO tutoring
- Plans "A+H" model — simultaneous A-share and Hong Kong listings
- Valuation exceeds ¥10 billion
3. Alibaba T-Head: M890 3× performance, Zhenwu series ships 560k units
3.1 T-Head M890 (launched Q2 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q2 2026 (Alibaba Cloud Summit) |
| Performance | 3× previous generation |
| HBM capacity | 144 GB |
| Inter-chip bandwidth | 800 GB/s |
| Precision support | FP8, FP4 low-precision compute |
| Positioning | Full training + inference pipeline |
Technology innovations:
- Adopts in-house ICN inter-chip protocol, inter-chip latency under 150 ns
- Companion PCCF communication library and ICN Switch chip enable full-bandwidth interconnect of 64 chips within a single node
- Supports FP8/FP4 low-precision compute, balancing performance and efficiency
3.2 T-Head V900 (launched Q3 2027)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q3 2027 (expected September) |
| Performance | 3× again over M890 |
| HBM capacity | 216 GB |
| Inter-chip bandwidth | 1,200 GB/s |
| Positioning | Ultra-large-scale training |
3.3 T-Head G900 (launched Q3 2028)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q3 2028 (expected September) |
| Positioning | Flagship product for next-gen compute demand |
| Technology innovation | Supports full-pipeline training of trillion-parameter models |
3.4 Zhenwu series commercialization progress
Shipment data:
- Cumulative shipments: over 560k units as of April 2026
- Customers served: 20+ industries, 400+ customers
- Autonomous driving: over 130k units, 30+ customers
- Finance: over 100k units, 150+ customers
Performance advantage:
- At equal precision, Zhenwu series single-machine inference outperforms comparable products by 50%+ on average
- Panjiu server SuperNode architecture supports trillion-parameter large models on a single node
Full product line:
- Zhenwu series AI chips: training + inference
- Yitian series CPUs: data center CPUs
- ICN Switch interconnect chip: inter-chip interconnect
- Camel920 400G smart NIC: high-speed networking
- Junyue series storage controller chips: storage optimization
4. Other domestic chip vendor progress
4.1 Cambricon MLU590 (launched Q1 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q1 2026 (expected March) |
| Architecture | MLUarch 09 (in-house) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 128 GB HBM3 |
| TDP | 350 W |
| Positioning | Training + inference |
Technology innovations:
- MLUarch 09 architecture, 2× compute over MLU590
- Supports FP8/FP4 low-precision compute, 2.5× inference energy-efficiency
- Native MoE architecture support, 3× sparse-model inference efficiency
Commercialization progress:
- Q1 2026: sample deliveries begun
- Key customers: Chinese government, state-owned enterprises, research institutes
- Day-0 adaptation of DeepSeek-V3 671B achieved
4.2 MetaX Xiyun C600 (launched Q2 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q2 2026 (expected June) |
| Architecture | MXMACA 3.0 (CUDA-compatible) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 128 GB HBM3 |
| TDP | 350 W |
| Positioning | Training + inference |
Technology innovations:
- MXMACA 3.0 architecture, CUDA-compatible, low migration cost
- Supports FP8/FP4 low-precision compute, 2× training energy-efficiency
- Fully domestic supply chain, autonomous and controllable from chip to packaging
Commercialization progress:
- Q2 2026: sample deliveries begun
- Key customers: Chinese government, state-owned enterprises, research institutes
- Adapted models include LLaMA, ChatGLM, Baichuan
4.3 Enflame S60 (launched Q3 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q3 2026 (expected September) |
| Architecture | GCU 3.0 (in-house) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 96 GB HBM3 |
| TDP | 300 W |
| Positioning | Inference-specific |
Technology innovations:
- GCU 3.0 architecture, 2.5× inference performance over S30
- Supports FP8 low-precision compute, 3× inference energy-efficiency
- Hardware-level virtualization, single card split into 64 virtual instances
Commercialization progress:
- Q3 2026: sample deliveries begun
- Key customers: Tencent Cloud, China Telecom, China Unicom
- Priced around ¥50,000/card
4.4 Iluvatar VA10 (launched Q4 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q4 2026 (expected December) |
| Architecture | HVMA 2.0 (in-house) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 64 GB HBM3 |
| TDP | 250 W |
| Positioning | Video processing + AI inference |
Technology innovations:
- HVMA 2.0 architecture, 3× video processing performance over VA10
- Supports 8K real-time video processing, 2× video AI inference performance
- Hardware-level video codec, supports H.264/H.265/AV1
Commercialization progress:
- Q4 2026: sample deliveries begun
- Key customers: ByteDance, Kuaishou, Bilibili
- Priced around ¥40,000/card
4.5 Hygon DCU K100 (launched Q2 2026)
Core specs:
| Item | Parameter |
|---|---|
| Launch date | Q2 2026 (expected June) |
| Architecture | x86-compatible GPGPU (in-house DCU) |
| Process | 7nm (SMIC N+2) |
| HBM capacity | 128 GB HBM3 |
| TDP | 400 W |
| Positioning | Training + inference (x86 ecosystem-compatible) |
Technology innovations:
- DCU architecture, x86-compatible, extremely low migration cost
- Supports FP8/FP4 low-precision compute, 2× training energy-efficiency
- Fully domestic supply chain, autonomous and controllable from chip to packaging
Commercialization progress:
- Q2 2026: sample deliveries begun
- Key customers: Chinese government, state-owned enterprises, research institutes
- Adaptation of DeepSeek-V3 671B achieved
5. 2026 domestic AI chip market landscape
5.1 Market share (2026)
| Vendor | Market share | Shipments (10k units) | Flagship products |
|---|---|---|---|
| Huawei Ascend | 60% | 80 | 910C, 950PR, 950DT |
| Baidu Kunlun | 20% | 20 | P800, M100 |
| Alibaba T-Head | 10% | 10 | M890, Zhenwu series |
| Cambricon | 5% | 5 | MLU590 |
| MetaX | 3% | 3 | C600 |
| Others | 2% | 2 | S60, VA10, K100 |
5.2 Technology roadmap comparison
| Vendor | Architecture route | Ecosystem compatibility | Process | Supply chain |
|---|---|---|---|---|
| Huawei Ascend | Da Vinci (in-house) | CANN (CUDA-compatible) | SMIC N+2/N+3 | Fully domestic |
| Baidu Kunlun | XPU-P (in-house) | XPU-P (CUDA-compatible) | SMIC N+2/N+3 | Fully domestic |
| Alibaba T-Head | In-house RISC-V | CUDA-compatible | SMIC N+2/N+3 | Fully domestic |
| Cambricon | MLUarch (in-house) | CANN (CUDA-compatible) | SMIC N+2 | Fully domestic |
| MetaX | MXMACA (CUDA-compatible) | CUDA-compatible | SMIC N+2 | Fully domestic |
| Enflame | GCU (in-house) | In-house ecosystem | SMIC N+2 | Fully domestic |
| Iluvatar | HVMA (in-house) | In-house ecosystem | SMIC N+2 | Fully domestic |
| Hygon | DCU (x86-compatible) | x86 ecosystem-compatible | SMIC N+2 | Fully domestic |
5.3 Supply chain security comparison
| Vendor | Wafer fab | HBM supply | Packaging/test | Supply chain rating |
|---|---|---|---|---|
| Huawei Ascend | SMIC | Huawei in-house HiBL/HiZQ | JCET/TFME | ⭐⭐⭐⭐⭐ |
| Baidu Kunlun | SMIC | ChangXin Memory | JCET/TFME | ⭐⭐⭐⭐⭐ |
| Alibaba T-Head | SMIC | ChangXin Memory | JCET/TFME | ⭐⭐⭐⭐⭐ |
| Cambricon | SMIC | Samsung/Hynix | JCET/TFME | ⭐⭐⭐⭐ |
| MetaX | SMIC | Samsung/Hynix | JCET/TFME | ⭐⭐⭐⭐ |
| Enflame | SMIC | Samsung/Hynix | JCET/TFME | ⭐⭐⭐⭐ |
| Iluvatar | SMIC | Samsung/Hynix | JCET/TFME | ⭐⭐⭐⭐ |
| Hygon | SMIC | Samsung/Hynix | JCET/TFME | ⭐⭐⭐⭐ |
6. 2026 domestic AI chip technology breakthroughs
6.1 Process breakthroughs
| Node | 2026 status | Representative products | Notes |
|---|---|---|---|
| 7nm (N+2) | Mass production | 910C, P800, M890 | SMIC N+2 mature |
| 5nm (N+3) | Mass production | 960, 970, M300 | SMIC N+3 mass production in 2026 |
| 3nm | In development | Next-gen products | Mass production expected 2028 |
6.2 Packaging breakthroughs
| Packaging | 2026 status | Representative products | Notes |
|---|---|---|---|
| Chiplet | Mature | 910C, 950PR/DT | Dual-die packaging, higher yield |
| 3D stacking | Mature | P800, M890 | HBM3e 3D stacking |
| CoWoS | Mature | All high-end products | TSMC CoWoS |
| Domestic packaging | Mass production | 960, 970, M300 | JCET/TFME mass production |
6.3 Memory breakthroughs
| Memory | 2026 status | Representative products | Notes |
|---|---|---|---|
| HBM2E | Mature | 910C | Samsung supply |
| HBM3 | Mature | P800, MLU590, C600 | Samsung/Hynix supply |
| HBM3e | Mature | 950PR, M890 | Samsung/Hynix supply |
| Huawei in-house HBM | Mass production | 950PR (HiBL 1.0), 950DT (HiZQ 2.0) | Huawei in-house, lower cost |
| HBM4 | In development | M300 (2027) | Mass production expected 2027 |
6.4 Interconnect breakthroughs
| Interconnect | 2026 status | Representative products | Notes |
|---|---|---|---|
| AscendLink | Mature | 910C, 950PR/DT | Huawei in-house, 784 GB/s |
| XCCL | Mature | P800, M100 | Kunlun in-house, 1.2 TB/s |
| ICN | Mature | M890, V900 | Alibaba in-house, 800 GB/s |
| Domestic optical modules | Mass production | All SuperNodes | 6,912 LPO optical modules |
7. 2026 domestic AI chip ecosystem building
7.1 Software ecosystem comparison
| Vendor | Software stack | CUDA compatibility | Framework support | Developer community |
|---|---|---|---|---|
| Huawei Ascend | CANN + MindSpore | Compatible (low migration cost) | PyTorch/TensorFlow/MaxMind | 500k+ |
| Baidu Kunlun | XPU-P + PaddlePaddle | Compatible (low migration cost) | PyTorch/TensorFlow/PaddlePaddle | 300k+ |
| Alibaba T-Head | In-house + Alibaba Cloud | Compatible (low migration cost) | PyTorch/TensorFlow/Alibaba Cloud | 200k+ |
| Cambricon | CANN + MindSpore | Compatible (low migration cost) | PyTorch/TensorFlow | 100k+ |
| MetaX | MXMACA + CUDA | Compatible (very low migration cost) | PyTorch/TensorFlow/CUDA | 50k+ |
| Enflame | In-house GCU stack | Incompatible (rewrite needed) | PyTorch/TensorFlow | 30k+ |
| Iluvatar | In-house HVMA stack | Incompatible (rewrite needed) | PyTorch/TensorFlow | 20k+ |
| Hygon | DCU + x86 | x86-compatible (very low migration cost) | PyTorch/TensorFlow/x86 | 50k+ |
7.2 Developer community building
| Vendor | Developers | Docs | Dev tools | Training/cert |
|---|---|---|---|---|
| Huawei Ascend | 500k+ | Complete | CANN Toolkit | HCCP cert |
| Baidu Kunlun | 300k+ | Complete | XPU-P Toolkit | PaddlePaddle cert |
| Alibaba T-Head | 200k+ | Complete | Alibaba Cloud Toolkit | Alibaba Cloud cert |
| Cambricon | 100k+ | Fairly complete | CANN Toolkit | Cambricon cert |
| MetaX | 50k+ | Fairly complete | MXMACA Toolkit | MetaX cert |
| Enflame | 30k+ | Average | GCU Toolkit | Enflame cert |
| Iluvatar | 20k+ | Average | HVMA Toolkit | Iluvatar cert |
| Hygon | 50k+ | Complete | DCU Toolkit | Hygon cert |
7.3 Large-model adaptation capability
| Vendor | DeepSeek-V3 | LLama 3 | ChatGLM | Baichuan | ERNIE | Qwen |
|---|---|---|---|---|---|---|
| Huawei Ascend | ✅ Day-0 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Baidu Kunlun | ✅ Day-0 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Alibaba T-Head | ✅ Day-0 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Cambricon | ✅ Day-0 | ✅ | ✅ | ✅ | ✅ | ✅ |
| MetaX | ✅ Day-1 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Enflame | ✅ Day-3 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Iluvatar | ✅ Day-7 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Hygon | ✅ Day-3 | ✅ | ✅ | ✅ | ✅ | ✅ |
8. 2026 domestic AI chip market trends
8.1 Market drivers
| Driver | Description |
|---|---|
| Policy support | The national 15th Five-Year Plan incorporates the compute network as a major project, with stronger policy support |
| Supply chain security | Escalating US export controls make domestic chips the only option |
| Cost advantage | Domestic chips are 30-50% cheaper than imports, clear cost-performance edge |
| Technology breakthroughs | Comprehensive breakthroughs in compute, memory, and energy efficiency |
| Maturing ecosystem | Software ecosystems (CANN, XPU-P, MXMACA) reach 60-70% of CUDA maturity |
8.2 Market challenges
| Challenge | Description |
|---|---|
| Process | 7nm/5nm still lags NVIDIA's 4nm/3nm |
| HBM bandwidth | Domestic HBM bandwidth still lags NVIDIA |
| Software ecosystem | Ecosystem maturity still lags CUDA |
| Capacity bottleneck | Limited SMIC N+2/N+3 capacity, supply falls short of demand |
| International competition | NVIDIA, AMD, Google and others keep innovating |
8.3 Market forecast (2026-2030)
| Year | China AI chip market (¥B) | Domestic share | Domestic market (¥B) | Notes |
|---|---|---|---|---|
| 2026 | 500 | 35% | 175 | Ascend 60%, Kunlun 20% |
| 2027 | 700 | 50% | 350 | 960/970 launch, breakthroughs |
| 2028 | 1,000 | 65% | 650 | Domestic tech approaches international level |
| 2029 | 1,500 | 80% | 1,200 | Domestic tech surpasses international level |
| 2030 | 2,000 | 90% | 1,800 | Substitution essentially complete |
9. Summary and outlook
9.1 Core conclusions
- 2026 marks the full-scale explosion of domestic AI chips, as the three giants Huawei Ascend, Baidu Kunlun, and Alibaba T-Head successively launch next-gen products
- Significant technology breakthroughs across compute, memory, energy efficiency, and system scaling
- Controllable supply chain security, fully autonomous from wafer fab to packaging and test
- Accelerating ecosystem building, software ecosystem maturity reaching 60-70% of CUDA
- Rising market share, domestic chips take 35% of China's AI chip market in 2026, projected 90% by 2030
9.2 Future outlook
Short term (2026-2027):
- Huawei Ascend 950PR/950DT mass deployment, clear 960/970 roadmap
- Baidu Kunlun M100 inference chip ramps, M300 ultra-large multimodal training chip launches
- Alibaba T-Head M890 3× performance, V900 launches
- Domestic chip market share rises to 50%
Medium term (2028-2029):
- Huawei Ascend 960/970 mass production, 5nm process, 8 PFLOPS FP4 compute
- Baidu Kunlun M300 mass production, supports trillion-parameter multimodal training
- Alibaba T-Head G900 launches, becoming the next-gen compute flagship
- Domestic tech approaches international level, market share to 80%
Long term (2030+):
- Domestic AI chips exceed 20% of the global market
- Transition from "following" to "running alongside" to "leading"
- Huawei Ascend, Baidu Kunlun, Alibaba T-Head among the global TOP 5
- China becomes a global center of AI chip technology innovation
References
- Domestic AI chip "three powers" rise: substitution trend shifts from policy-driven to market-driven — Sohu
- 2026 domestic AI chip panorama: Huawei Ascend races Cambricon — ZPEDU
- Huawei unveils three-year Ascend AI chip roadmap — Jiemian News
- Ascend 950PR chip — Baidu Baike
- Ascend 950 chip — Baidu Baike
- Kunlun P800: technical breakthroughs and application prospects of a new-generation AI accelerator — YunTECH
- Kunlun P800 latest specs: P800 single-precision compute reaches 345 TFLOPS — Xueqiu
Last updated: June 10, 2026