Analysis of the Impact of NVIDIA Rubin Platform Mass Production on China's AI Industry and Response Strategies

Based on the latest industry data and market analysis, this report systematically elaborates on the impact of NVIDIA Rubin Platform mass production on China’s AI industry and corresponding response strategies.

The NVIDIA Rubin Platform entered full mass production in Q1 2026[1], achieving significant performance improvements compared to the previous-generation Blackwell platform:

Metric	Blackwell	Rubin	Improvement Multiple
Inference Performance	10 PFLOPs	50 PFLOPs (NVFP4)	5x
Training Performance	10 PFLOPs	35 PFLOPs (NVFP4)	3.5x
HBM4 Memory Bandwidth	8 TB/s	22 TB/s	2.8x
NVLink Bandwidth	1.8 TB/s	3.6 TB/s	2x
Per-Token Cost	Baseline	-	1/10

The Rubin Platform uses six new chips, including the Vera CPU, Rubin GPU, NVLink 6 Switch, etc., and achieves system-level performance optimization through “extreme co-design”[2][3].

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Analyzed from the TPP (Total Processing Performance) dimension, the gap between China’s mainstream AI chips and NVIDIA’s remains significant:

Chip Manufacturer	Representative Product	TPP Estimate	Mass Production Status
NVIDIA	H100	8,192	Mass Produced
NVIDIA	H200	15,840	Mass Produced
NVIDIA	Rubin	~50,000	Q1 2026 Mass Production
Huawei	Ascend 910B	5,120	Mass Produced
Huawei	Ascend 910C	12,032	Q4 2024 Mass Production
Cambricon	Siyuan 590	4,493	Engineering Sample
KunlunXin	P800	~4,800	Small-Batch Mass Production

: Among current domestic AI chips, only Huawei’s Ascend 910C can approach the H200 level; the mass production of the Rubin Platform will widen this gap to [4].

According to IDC data, the Chinese AI accelerator chip market showed the following pattern in H1 2025:

• NVIDIA Market Share
  : ~62%
• Domestic Chip Share
  : ~35% (25% in the same period of 2024)
• Projection by End of 2025
  : Domestic chips are expected to exceed 50% market share[5]

However, the mass production of the Rubin Platform may slow down this domestic substitution process. Notably, NVIDIA’s revenue from China has plummeted from 22.1% in Q1 2023 to 5.3% in Q3 2025[6], reflecting the impact of export control policies.

Industrial Tier	Degree of Impact	Feature Description
Large Model Training	Extremely High	Rigid demand for high-end computing power, Rubin brings generational gap
Inference Applications	Medium	High cost sensitivity, large space for domestic substitution
Edge/Device-Side	Low	Scenario differentiation, Chinese manufacturers have advantages
System Integration	Medium	Hardware-software collaboration becomes a key competitive factor

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Facing the widening computing power gap, Chinese enterprises are adopting multi-dimensional, hierarchical response strategies:

┌─────────────────────────────────────────────────────┐
│           System-Level Optimization Technology Roadmap │
├─────────────────────────────────────────────────────┤
│  • Huawei HCCS Interconnect Technology: Inter-chip bandwidth up to 100GB/s │
│  • Cluster Scale Expansion: Ascend 910C supports 384-card clusters │
│  • Heterogeneous Computing: CANN architecture enables CPU/NPU collaboration │
│  • Software Optimization: Deep adaptation of the MindSpore framework │
└─────────────────────────────────────────────────────┘

:

• Open-source models such as DeepSeek
  : Reduce computing power requirements through techniques like model compression, sparse computing, and mixed-precision training, achieving performance close to international advanced levels with existing hardware[7]
• Software Ecosystem Collaboration
  : Huawei Ascend, Cambricon, KunlunXin, etc., have achieved rapid deployment and inference optimization for the DeepSeek model

Strategy Dimension	Specific Measures	Representative Enterprises
Inference Chips	Focus on energy efficiency ratio optimization to replace “brute-force computing”	CloudWalk Technology, Cambricon
Architecture Innovation	Da Vinci Architecture 3D Cube Matrix Computing Unit	Huawei Ascend
Full-Stack Capabilities	Vertical integration from chips to frameworks to applications	Huawei, Baidu KunlunXin
Scenario Deep Cultivation	Vertical fields such as smart cities, industrial manufacturing	Multiple Enterprises

: In the inference era, “everyone stands on the same new starting line”; whoever can build advantages in cost, efficiency, and system capabilities will have opportunities[8].

Technology Direction	Current Progress	Development Goal
Advanced Manufacturing Processes	7nm domesticization breakthrough	Mass production of 5nm and below
Independent Ecosystem	Frameworks such as MindSpore and PaddlePaddle are mature	Improve domestic software stacks
Device-Side AI	Penetration into consumer electronics scenarios	Popularized applications
Standard Leadership	Domestic standard formulation	Participate in international standards

---

:

• “East Digital West Computing” Project: Construction of 8 national computing power hub nodes
• “AI+” Initiative: Application implementation in 6 key industries
• Domestic Substitution Policy: Priority procurement of domestic computing power in government and enterprise markets[9]

:

• Special Support for the Semiconductor Industry: Tax incentives, R&D subsidies
• Capital Market Favor: Moore Threads soared 425% on its first trading day, and Muxi Semiconductor saw unprecedented subscription enthusiasm[10]

Domestic Computing Power Industrial Ecosystem Matrix
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Chip Layer │  Huawei Ascend, Cambricon, KunlunXin, Moore Threads
  ─────────┼───────────────────────────────────
  Server Layer │  Sugon, Inspur, Foxconn Industrial Internet
  ─────────┼───────────────────────────────────
  Software Layer │  MindSpore, Baidu PaddlePaddle
  ─────────┼───────────────────────────────────
  Application Layer │  Smart Cities, FinTech, Intelligent Manufacturing
  ─────────┼───────────────────────────────────
  Support Layer │  Liquid Cooling Technology (Envicool, Sugon Chuangxin), Optical Modules
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Metric	2024	2025	Growth Rate
China’s AI Server Market Size	~$16 billion	Projected $32 billion	+100%
Daily Token Consumption	100 billion	30 trillion	+300x
Domestic Chip Computing Power Share	33%	Projected >50%	Significant Increase

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Dimension	Current Situation Assessment	Development Trend
Computing Power Gap	3-5 year generational gap exists	May widen further
Substitution Process	Accelerating	Requires continuous investment
Competitive Advantages	Scenario applications, cost advantages	Opportunities in the inference era
Technological Breakthroughs	System-level innovation is active	Need to break through process bottlenecks

:

• Continue to increase R&D investment in advanced manufacturing processes
• Improve domestic computing power procurement policies
• Promote cross-industry application scenario opening

:

• Short-term: Embrace open-source ecosystems to reduce computing power dependence
• Medium-term: Focus on inference scenarios to build differentiated advantages
• Long-term: Participate in full-stack ecosystem construction to build moats

:

• Focus on enterprises with both “hardware + software ecosystem” capabilities
• Attach importance to leading manufacturers with mass production capabilities
• Lay out device-side AI and vertical application tracks

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[1] NVIDIA. “NVIDIA Kicks Off the Next Generation of AI With Rubin.” NVIDIA News, January 2026. https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer

[2] HPC Wire. “Nvidia Says Rubin Will Deliver 5x AI Inference Boost Over Blackwell.” January 5, 2026. https://www.hpcwire.com/2026/01/05/nvidia-says-rubin-will-deliver-5x-ai-inference-boost-over-blackwell/

[3] VideoCardz. “NVIDIA Vera Rubin NVL72 Detailed: 72 GPUs, 36 CPUs.” January 2026. https://videocardz.com/newz/nvidia-vera-rubin-nvl72-detailed-72-gpus-36-cpus-260-tb-s-scale-up-bandwidth

[4] Wall Street CN. “How Can China’s Computing Power Grow Stronger?” January 4, 2026. https://wallstreetcn.com/articles/3762510

[5] IDC. “2025 H1 China Accelerator Chip Market Data.” Cited from financial media reports.

[6] The Street. “Nvidia’s China chip problem isn’t what most investors think.” December 2025. https://www.thestreet.com/investing/nvidias-china-chip-problem-isnt-what-most-investors-think

[7] Eastmoney. “The Chinese Narrative of Joint Breakthroughs for Large Models and AI Chips.” January 2026. https://finance.eastmoney.com/a/202601033607637784.html

[8] Caifuhao. “General-Purpose GPU vs. Inference Chip: From Computing Power Competition to Efficiency Breakthrough.” January 2026. https://caifuhao.eastmoney.com/news/20260106184911384212320

[9] Sina Finance. “Policy Sets Tone for the Arrival of the ‘AI+’ Era.” December 2025. https://finance.sina.com.cn/roll/2025-12-20/doc-inhcmnmm6768672.shtml

[10] Tencent News. “A-Share Computing Power in 2025: Industrial Advancement Under Capital Narrative Reconstruction.” December 2025. https://news.qq.com/rain/a/20251226A06LHW00