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3 posts tagged with "昆仑芯M100"

百度昆仑芯M100/M300新一代芯片

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昆仑芯 M100 部署进展与百度自研芯片路线

· 7 min read
Industry Research Team

2026 年 6 月 12 日,在智源大会上昆仑芯研发副总裁漆维透露,面向推理市场的 M100 芯片目前已交付大客户进行测试,并已向客户小规模出货。与寒武纪 MLU690 迟迟无法交付形成对照,昆仑芯的节奏明显更靠前——尽管它面临的最大问题是需求侧而非供给侧。

M100 的定位:只做推理​

昆仑芯 M100 是昆仑芯科技 2026 年推出的第四代 AI 芯片产品,延续自研 XPU 架构理念,主要针对大规模推理场景(尤其是 MoE 模型)进行优化设计,旨在提升此类模型的推理性能并提供较高的性价比。

这颗芯片的发布时间可以追溯到 2025 年 11 月 13 日的百度世界大会。百度集团执行副总裁沈抖透露将发布新一代昆仑芯,其中包括面向大规模推理的 M100 和面向超大规模训练与推理的 M300,计划分别于 2026 年年初和 2027 年年初上市。2026 年 1 月 27 日,百度 CEO 李彦宏在接受《时代》杂志专访时评论芯片行业,表示百度刚刚发布了新款 M100 芯片,并正在开发新的芯片集群。

在百度 2026 年的业绩会上,李彦宏进一步披露昆仑芯已经完成三代 AI 芯片的研发与商业化,并公布了 M100 与 M300 的产品路线图。

维度昆仑芯 M100昆仑芯 M300
上市时间2026 年年初2027 年年初
目标场景大规模推理,尤其是 MoE 模型超大规模多模态模型训练与推理
部署状态2026 年 6 月已交付大客户测试并小规模出货尚未发布
配套超节点天池 256 / 天池 512规划中

值得注意的是,2026 年 7 月百度昆仑芯科技自主研发新品 M100 芯片实物首次在央视报道中公开亮相——这类"实物亮相"的报道时点,通常意味着产品已进入可交付状态。

天池超节点:从千柜级部署到开放规范​

M100 的商业化依托的是天池超节点。据公开报道,天池 256 与天池 512 超节点在 2025 年百度世界大会发布:最高支持 256 及 512 卡极速互联,卡间互联总带宽分别提升 4 倍及 1 倍,其中天池 256 主流大模型推理任务单卡 tokens 吞吐提升 3.5 倍,天池 512 靠单节点即可完成万亿参数模型训练。

这两款超节点计划分别于 2026 年上半年及下半年推出。天池超节点覆盖 32 至 512 卡全场景,采用标准机柜形态、三总线盲插与模块化设计,兼容液冷与风冷 IDC。

更重要的进展发生在 2026 年 9 月 16 日。据报道,百度智能云在 2026 智能经济论坛上宣布天池超节点已实现全栈国产,并已在多地智算中心进行千柜级规模部署,支撑万亿参数大模型训练;同时宣布全面开放天池超节点的架构、供电、散热、节点、互联和管理六大核心模块技术规范,助力国产芯片更快完成从单卡验证到超节点级系统适配。

这份规范开放的意味值得深挖:它与昇腾推动 NPO 成为 OIF 标准、摩尔线程联合中国移动研究院与之江实验室发布《OISA 高密超节点参考设计技术规范》指向同一件事——超节点的技术规范正在成为国产厂商争夺的新战场。

百度的芯片路线:五年五芯与集群梯度​

百度在芯片路线上的公开表述相当清晰。第一是产品化节奏,即"五年五芯"计划,推动每年推出新版芯片,把芯片开发模式从项目制转向产品化。第二是场景化细分策略:M100 专注提升大规模推理任务的性价比,M300 针对超大规模多模态模型优化。

第三是集群规模的梯度规划。据公开报道,百度智能云计划将昆仑芯单一集群规模从三万卡扩展至百万卡级别;另有报道称百度正在研发显卡级超级节点,预计 2027 年下半年开始交付千卡至四千卡规模的计算集群,并已有四千卡超节点的规划。

现有的部署规模方面,据公开报道,2026 年上半年百度点亮了昆仑芯 P800 的 3 万卡机,这是国产算力发展的重要里程碑;昆仑芯累计已完成数万卡部署。外部落地方面,昆仑芯已进入招商银行大模型算力项目,并在国家电网、中钢研完成部署,且已构建覆盖互联网、能源、金融、制造、汽车、通信、教育等行业的全栈昆仑芯集群。

在安全认证上,据公开报道,昆仑芯 M100 在最新一轮安全可靠测评中获得 Level I 评级,同期华为昇腾 950 获得 Level II 评级,龙芯与海思鲲鹏的通用处理器则获得最高等级的 Level III 评级。该三级体系正在塑造政府采购的实质性导向。需要注意,测评结果不等于性能结论——官方报道也指出 M100 较低的评级意味着它在满足该框架最严苛基准上仍有距离。

研判​

昆仑芯当前的处境与华为、寒武纪、摩尔线程都不同:它不缺乏产能,也不缺乏标杆模型,它缺的是足够大的外部需求。据公开报道的行业调研,昆仑芯自用需求 2026 年无明显提升,P800 和 M100 需求趋于饱和,更多是借 AI 软件影响力做生态推广;对外主要靠推理业务与百度软件绑定,面向政府、教育、政务等传统政企行业,侧重点在生态渗透而非自身业务放量。

这解释了百度为何要开放天池超节点的六大核心模块技术规范——当自家消纳能力见顶,向外输出系统规范与工具链就成了打开市场的方式。而安全可靠测评中 M100 仅获 Level I,也说明政企市场的主流通道目前更偏向评级更高的昇腾。

昆仑芯的战略价值因此不在短期营收,而在于它把国产自研芯片的"第三条路"走通了:不与华为拼超节点规模,也不与寒武纪拼产能,而是绑定云平台做算力输出。如果 M100 在推理侧把性价比做实,并借天池规范形成生态飞轮,它将在国产算力市场占据一个与华为错位的位置——用推理性价比与云服务捆绑,而不是用训练规模与系统架构正面对抗。M300 在 2027 年的表现,才是这条路线的最终答卷。

昆仑芯 M100/M300 深度解析:百度三代自研的集大成

· 4 min read
Industry Research Team

2025 年 11 月 13 日的百度世界大会上,昆仑芯 M100 与 M300 正式亮相,同时发布的还有天池 256 与天池 512 超节点。这不是百度第一次发布自研芯片,却是第一次以完整路线图的形式,宣告芯片、超节点、集群三层算力体系的未来五年规划。大厂自研芯片由此进入深水区。

十四年积累:从昆仑 1 到 P800​

昆仑芯的历史可追溯到 2011 年百度内部的数据中心加速器研发。2018 年第一代昆仑 1(818-300)发布,基于三星 14nm 工艺与自研 XPU 架构,INT8 算力 260 TOPS;2021 年第二代昆仑 2 采用 7nm 工艺,R300 加速卡提供 128 TFLOPS FP16 与 256 TOPS INT8,150W 功耗,并完成独立融资。2024 年 3 月发布的 P800 是当前部署主力:96GB HBM 显存、2.4 TB/s 带宽、FP16 算力 345 TFLOPS、400W 功耗。据 IDC 数据,2024 年昆仑芯出货 6.9 万片,位居国产 AI 芯片厂商第二,仅次于华为昇腾。

型号发布时间关键规格定位
昆仑 1(818-300)2018 年 7 月260 TOPS INT8,14nm首代云端推理
昆仑 2(R300)2021 年 8 月128 TFLOPS FP16,32GB GDDR6训练推理通用
P8002024 年 3 月345 TFLOPS FP16,96GB HBM,2.4 TB/s万卡集群主力
M1002026 年上市面向 MoE 推理优化大规模推理
M3002027 年初上市峰值算力 1000 TOPS 量级(据公开报道)超大规模多模态训推

M100:为 Token 时代设计的推理芯​

M100 是昆仑芯科技第四代产品,延续自研 XPU 架构理念,针对大规模推理场景做了专项设计:通过革新资源架构大幅提升 MoE(混合专家)模型的推理性能,主打极致性价比。它于 2026 年初进入商业放量期,已交付大客户测试并小规模出货,2026 年 7 月芯片实物在央视报道中首次公开亮相。高盛预计昆仑芯 2025 年销售额约 35 亿元,2026 年有望跃升至 65 亿元。

与 M100 配套的天池超节点同样关键:天池 256 最高支持 256 卡极速互联,卡间互联带宽较上代提升 4 倍、整体性能提升 50%;天池 512 单个超节点即可支撑万亿参数模型训练。百度已点亮昆仑芯三万卡集群,计划将单一集群规模扩展至百万卡级别。

M300 与路线图:每年一代的承诺​

M300 面向超大规模多模态模型的训练与推理,针对视频生成、3D 建模等任务专项优化,据公开报道其峰值算力可达 1000 TOPS 量级,训练效率较通用方案有望提升 3 倍以上,计划 2027 年初上市。后续路线图同样清晰:2028 年天池千卡级超节点、2029 年昆仑芯 N 系列、2030 年百万卡单集群点亮。未来五年,昆仑芯承诺每年推出新产品。

资本层面,昆仑芯已于 2026 年 1 月向港交所提交上市申请,并开启科创板上市辅导,有券商对其估值给出约 440 亿美元的高预期。分拆上市后,昆仑芯将更多以"中立供应商"身份服务外部客户——事实上其外部业务比重已超过向百度内部供货。

小结:大厂自研的正确姿势​

昆仑芯的价值在于样本意义:它证明了大厂自研芯片可以从内部工具成长为独立商业体。M100 承接推理放量的当下,M300 冲刺训练上限的未来,天池超节点补上系统级短板,三层协同构成完整拼图。对于既需要稳定算力供给、又希望绑定长期演进路线的用户,这套"芯片加超节点加云平台"的全栈方案,是国产算力市场里确定性最高的选项之一。

2026 Domestic AI Chip Progress: Huawei Ascend 950, Baidu Kunlun M100, Alibaba T-Head M890 Fully Explained

· 15 min read
Industry Research Team

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:

ItemParameter
Launch dateMarch 21, 2026
PlatformAtlas 350 accelerator card
HBM capacity128 GB (Huawei in-house HiBL 1.0 HBM)
Memory bandwidth1.6 TB/s
FP8 compute1 PFLOPS
PositioningInference-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:

ItemParameter
Launch dateQ4 2026 (expected October)
HBM capacity144 GB (Huawei in-house HiZQ 2.0 HBM)
Memory bandwidth4 TB/s (HiZQ 2.0 technology)
FP8 compute1 PFLOPS
PositioningInference + training (Decode stage + training tasks)
Technology innovationFirst 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:

ItemConfiguration
Max interconnected chips8,192 chips
Total FP8 compute1 EFLOPS (1,024-card scale)
1024-card version16 liquid-cooled cabinets, 64 chips per cabinet
Supported modelsTrillion-parameter large-model training
Deployment progress1,024-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 modelLaunchCore specsPositioning
Ascend 960Q4 2027N+3 process, 288GB HBM, FP8 2 PFLOPS, 30%+ better energy efficiency than 910CUltra-large-scale training
Ascend 970Q4 2028N+3 process, FP4 8 PFLOPS, 4 TB/s bandwidth, supports trillion-parameter modelsNext-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:

ItemParameter
Launch dateEarly 2026 (expected Q2)
PositioningInference-specific
ArchitectureIn-house XPU-P architecture (inference-optimized)
Process7nm (SMIC N+2)
HBM capacity64 GB (inference-optimized)
TDP250 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:

ItemParameter
Launch dateQ1 2027 (expected March)
PositioningUltra-large-scale multimodal training
ArchitectureIn-house XPU-P architecture (multimodal-optimized)
Process5nm (SMIC N+3)
HBM capacity256 GB HBM4
TDP500 W
ModalitiesText, 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:

ItemConfiguration
Launch dateJune 2026 (expected)
Chip count256 Kunlun P800/M100
Effective training rate97%
Inter-chip bandwidth1.2 TB/s
Validated modelsBaidu 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:

ItemParameter
Launch dateQ2 2026 (Alibaba Cloud Summit)
Performance3× previous generation
HBM capacity144 GB
Inter-chip bandwidth800 GB/s
Precision supportFP8, FP4 low-precision compute
PositioningFull 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:

ItemParameter
Launch dateQ3 2027 (expected September)
Performance3× again over M890
HBM capacity216 GB
Inter-chip bandwidth1,200 GB/s
PositioningUltra-large-scale training

3.3 T-Head G900 (launched Q3 2028)​

Core specs:

ItemParameter
Launch dateQ3 2028 (expected September)
PositioningFlagship product for next-gen compute demand
Technology innovationSupports 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:

ItemParameter
Launch dateQ1 2026 (expected March)
ArchitectureMLUarch 09 (in-house)
Process7nm (SMIC N+2)
HBM capacity128 GB HBM3
TDP350 W
PositioningTraining + 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:

ItemParameter
Launch dateQ2 2026 (expected June)
ArchitectureMXMACA 3.0 (CUDA-compatible)
Process7nm (SMIC N+2)
HBM capacity128 GB HBM3
TDP350 W
PositioningTraining + 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:

ItemParameter
Launch dateQ3 2026 (expected September)
ArchitectureGCU 3.0 (in-house)
Process7nm (SMIC N+2)
HBM capacity96 GB HBM3
TDP300 W
PositioningInference-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:

ItemParameter
Launch dateQ4 2026 (expected December)
ArchitectureHVMA 2.0 (in-house)
Process7nm (SMIC N+2)
HBM capacity64 GB HBM3
TDP250 W
PositioningVideo 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:

ItemParameter
Launch dateQ2 2026 (expected June)
Architecturex86-compatible GPGPU (in-house DCU)
Process7nm (SMIC N+2)
HBM capacity128 GB HBM3
TDP400 W
PositioningTraining + 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)​

VendorMarket shareShipments (10k units)Flagship products
Huawei Ascend60%80910C, 950PR, 950DT
Baidu Kunlun20%20P800, M100
Alibaba T-Head10%10M890, Zhenwu series
Cambricon5%5MLU590
MetaX3%3C600
Others2%2S60, VA10, K100

5.2 Technology roadmap comparison​

VendorArchitecture routeEcosystem compatibilityProcessSupply chain
Huawei AscendDa Vinci (in-house)CANN (CUDA-compatible)SMIC N+2/N+3Fully domestic
Baidu KunlunXPU-P (in-house)XPU-P (CUDA-compatible)SMIC N+2/N+3Fully domestic
Alibaba T-HeadIn-house RISC-VCUDA-compatibleSMIC N+2/N+3Fully domestic
CambriconMLUarch (in-house)CANN (CUDA-compatible)SMIC N+2Fully domestic
MetaXMXMACA (CUDA-compatible)CUDA-compatibleSMIC N+2Fully domestic
EnflameGCU (in-house)In-house ecosystemSMIC N+2Fully domestic
IluvatarHVMA (in-house)In-house ecosystemSMIC N+2Fully domestic
HygonDCU (x86-compatible)x86 ecosystem-compatibleSMIC N+2Fully domestic

5.3 Supply chain security comparison​

VendorWafer fabHBM supplyPackaging/testSupply chain rating
Huawei AscendSMICHuawei in-house HiBL/HiZQJCET/TFME⭐⭐⭐⭐⭐
Baidu KunlunSMICChangXin MemoryJCET/TFME⭐⭐⭐⭐⭐
Alibaba T-HeadSMICChangXin MemoryJCET/TFME⭐⭐⭐⭐⭐
CambriconSMICSamsung/HynixJCET/TFME⭐⭐⭐⭐
MetaXSMICSamsung/HynixJCET/TFME⭐⭐⭐⭐
EnflameSMICSamsung/HynixJCET/TFME⭐⭐⭐⭐
IluvatarSMICSamsung/HynixJCET/TFME⭐⭐⭐⭐
HygonSMICSamsung/HynixJCET/TFME⭐⭐⭐⭐

6. 2026 domestic AI chip technology breakthroughs​

6.1 Process breakthroughs​

Node2026 statusRepresentative productsNotes
7nm (N+2)Mass production910C, P800, M890SMIC N+2 mature
5nm (N+3)Mass production960, 970, M300SMIC N+3 mass production in 2026
3nmIn developmentNext-gen productsMass production expected 2028

6.2 Packaging breakthroughs​

Packaging2026 statusRepresentative productsNotes
ChipletMature910C, 950PR/DTDual-die packaging, higher yield
3D stackingMatureP800, M890HBM3e 3D stacking
CoWoSMatureAll high-end productsTSMC CoWoS
Domestic packagingMass production960, 970, M300JCET/TFME mass production

6.3 Memory breakthroughs​

Memory2026 statusRepresentative productsNotes
HBM2EMature910CSamsung supply
HBM3MatureP800, MLU590, C600Samsung/Hynix supply
HBM3eMature950PR, M890Samsung/Hynix supply
Huawei in-house HBMMass production950PR (HiBL 1.0), 950DT (HiZQ 2.0)Huawei in-house, lower cost
HBM4In developmentM300 (2027)Mass production expected 2027

6.4 Interconnect breakthroughs​

Interconnect2026 statusRepresentative productsNotes
AscendLinkMature910C, 950PR/DTHuawei in-house, 784 GB/s
XCCLMatureP800, M100Kunlun in-house, 1.2 TB/s
ICNMatureM890, V900Alibaba in-house, 800 GB/s
Domestic optical modulesMass productionAll SuperNodes6,912 LPO optical modules

7. 2026 domestic AI chip ecosystem building​

7.1 Software ecosystem comparison​

VendorSoftware stackCUDA compatibilityFramework supportDeveloper community
Huawei AscendCANN + MindSporeCompatible (low migration cost)PyTorch/TensorFlow/MaxMind500k+
Baidu KunlunXPU-P + PaddlePaddleCompatible (low migration cost)PyTorch/TensorFlow/PaddlePaddle300k+
Alibaba T-HeadIn-house + Alibaba CloudCompatible (low migration cost)PyTorch/TensorFlow/Alibaba Cloud200k+
CambriconCANN + MindSporeCompatible (low migration cost)PyTorch/TensorFlow100k+
MetaXMXMACA + CUDACompatible (very low migration cost)PyTorch/TensorFlow/CUDA50k+
EnflameIn-house GCU stackIncompatible (rewrite needed)PyTorch/TensorFlow30k+
IluvatarIn-house HVMA stackIncompatible (rewrite needed)PyTorch/TensorFlow20k+
HygonDCU + x86x86-compatible (very low migration cost)PyTorch/TensorFlow/x8650k+

7.2 Developer community building​

VendorDevelopersDocsDev toolsTraining/cert
Huawei Ascend500k+CompleteCANN ToolkitHCCP cert
Baidu Kunlun300k+CompleteXPU-P ToolkitPaddlePaddle cert
Alibaba T-Head200k+CompleteAlibaba Cloud ToolkitAlibaba Cloud cert
Cambricon100k+Fairly completeCANN ToolkitCambricon cert
MetaX50k+Fairly completeMXMACA ToolkitMetaX cert
Enflame30k+AverageGCU ToolkitEnflame cert
Iluvatar20k+AverageHVMA ToolkitIluvatar cert
Hygon50k+CompleteDCU ToolkitHygon cert

7.3 Large-model adaptation capability​

VendorDeepSeek-V3LLama 3ChatGLMBaichuanERNIEQwen
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✅✅✅✅✅

8.1 Market drivers​

DriverDescription
Policy supportThe national 15th Five-Year Plan incorporates the compute network as a major project, with stronger policy support
Supply chain securityEscalating US export controls make domestic chips the only option
Cost advantageDomestic chips are 30-50% cheaper than imports, clear cost-performance edge
Technology breakthroughsComprehensive breakthroughs in compute, memory, and energy efficiency
Maturing ecosystemSoftware ecosystems (CANN, XPU-P, MXMACA) reach 60-70% of CUDA maturity

8.2 Market challenges​

ChallengeDescription
Process7nm/5nm still lags NVIDIA's 4nm/3nm
HBM bandwidthDomestic HBM bandwidth still lags NVIDIA
Software ecosystemEcosystem maturity still lags CUDA
Capacity bottleneckLimited SMIC N+2/N+3 capacity, supply falls short of demand
International competitionNVIDIA, AMD, Google and others keep innovating

8.3 Market forecast (2026-2030)​

YearChina AI chip market (¥B)Domestic shareDomestic market (¥B)Notes
202650035%175Ascend 60%, Kunlun 20%
202770050%350960/970 launch, breakthroughs
20281,00065%650Domestic tech approaches international level
20291,50080%1,200Domestic tech surpasses international level
20302,00090%1,800Substitution essentially complete

9. Summary and outlook​

9.1 Core conclusions​

  1. 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
  2. Significant technology breakthroughs across compute, memory, energy efficiency, and system scaling
  3. Controllable supply chain security, fully autonomous from wafer fab to packaging and test
  4. Accelerating ecosystem building, software ecosystem maturity reaching 60-70% of CUDA
  5. 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​

  1. Domestic AI chip "three powers" rise: substitution trend shifts from policy-driven to market-driven — Sohu
  2. 2026 domestic AI chip panorama: Huawei Ascend races Cambricon — ZPEDU
  3. Huawei unveils three-year Ascend AI chip roadmap — Jiemian News
  4. Ascend 950PR chip — Baidu Baike
  5. Ascend 950 chip — Baidu Baike
  6. Kunlun P800: technical breakthroughs and application prospects of a new-generation AI accelerator — YunTECH
  7. Kunlun P800 latest specs: P800 single-precision compute reaches 345 TFLOPS — Xueqiu

Last updated: June 10, 2026