China's domestic AI chip makers are poised to control 90% of the country's AI accelerator market in 2026, a dramatic shift driven by U.S. export restrictions and Beijing's push to reduce dependence on American hardware. Just two years ago, Nvidia commanded 66% of China's AI chip market; by 2026, analysts expect the American company's share to plummet to just 8%.

What Happened to Nvidia's Dominance in China?

The collapse of Nvidia's market position in China tells a story of export controls reshaping global technology competition. In 2024, Nvidia held two-thirds of China's AI accelerator market. By 2025, that share had fallen to 40%, and the company did not officially ship any new accelerators to Chinese clients in the first half of 2026. Nvidia's chief executive Jensen Huang acknowledged this reality in May, stating that his company's market share in China was "zero" for new official shipments, though some older Nvidia GPUs continue to reach Chinese companies through unofficial channels because they remain dependent on Nvidia's CUDA software ecosystem.

The numbers reveal the scale of this transition. In 2025, China's total AI accelerator market reached 4 million units. Nvidia and AMD combined shipped 2.36 million units, commanding 59% of the market. To replace nearly all of that volume with domestic alternatives in just one year, China's semiconductor industry would need to increase AI accelerator output by more than 2.2 times compared to 2025 levels.

Which Chinese Companies Are Winning the AI Chip Race?

Two companies are expected to emerge as the biggest beneficiaries of this shift: Huawei and Cambricon. In 2025, Huawei shipped 812,000 AI accelerators and commanded 20.3% of China's market, making it Nvidia's closest competitor. Cambricon, by contrast, supplied only around 116,000 units in 2025, but analysts project dramatic growth ahead.

China is pursuing what analysts call a "dual-track strategy" to replace foreign chips. This approach combines merchant suppliers like Huawei and Cambricon with custom AI chips designed by hyperscale cloud providers including Alibaba, Baidu, ByteDance, and Tencent. Large cloud companies prefer building their own chips because custom silicon is cheaper than buying merchant accelerators and can be optimized for their specific workloads and data formats.

• Huawei: Shipped 812,000 AI accelerators in 2025 with 20.3% market share; developing Ascend 950-series accelerators using proprietary memory technologies
• Alibaba T-Head: Produced 265,000 AI accelerators in 2025, positioning itself as a mid-tier player in the domestic market
• Cambricon: Supplied 116,000 units in 2025 but expected to see substantial growth as government policy prioritizes domestic players
• Kunlunxin: Also supplied around 116,000 AI processors in 2025, competing alongside Cambricon for market share

What Are the Biggest Obstacles to China's AI Chip Independence?

Despite ambitious projections, China's semiconductor industry faces significant bottlenecks that could prevent it from fully replacing foreign chips. The most critical challenge is high-bandwidth memory, or HBM, a specialized type of memory that AI chips require to function at peak performance. Huawei has acquired substantial quantities of HBM2-class memory from Samsung, but supplies are limited. To address this, Huawei is developing proprietary memory technologies called HiBL 1.0 and HiZQ 2.0 instead of relying on industry-standard HBM2 or HBM3.

China's domestic DRAM champion, CXMT, is preparing to manufacture HBM3 memory in late 2026, but it remains unclear how quickly the company can scale production to meaningful levels. Additionally, Nvidia's CUDA software ecosystem represents another formidable advantage that cannot be quickly replicated. While Huawei opened its CANN software stack to accelerate development, the maturity and breadth of Nvidia's software tools give American chips a lasting edge even as Chinese hardware performance improves.

Manufacturing capacity presents a third constraint. SMIC, China's largest and most advanced foundry, reported a 36% year-over-year revenue increase in the second quarter of 2026, suggesting growing output and pricing power. However, whether SMIC can increase AI accelerator production by more than 2 times in a single year remains uncertain.

How Are Chinese AI Chips Performing Against Nvidia?

Chinese AI hardware has made substantial technical progress. Huawei's solutions can now match or exceed the performance of Nvidia's NVL72 GB200 rack-scale system, though at the cost of consuming significantly more power. For data center operators where power consumption is not a primary constraint, Huawei can build AI infrastructure with performance comparable to or better than Nvidia-based systems.

This performance parity is crucial because it means Chinese companies are no longer offering inferior alternatives; they are offering competitive solutions optimized for domestic use cases. As China's software ecosystem matures, new AI deployments increasingly rely on domestic software stacks rather than CUDA, reducing the switching costs that once locked customers into Nvidia's ecosystem.

Steps to Understanding China's AI Chip Strategy

• Track Market Share Shifts: Monitor quarterly reports from research firms like TrendForce and Bernstein to see whether Chinese vendors are actually achieving their projected 90% market share target or whether supply constraints limit growth
• Watch Memory Production Timelines: Follow announcements from CXMT and other Chinese memory makers about HBM3 manufacturing ramp-up, as memory availability will determine whether Chinese chip makers can meet demand
• Assess Software Ecosystem Maturity: Evaluate progress on CANN and other domestic AI software stacks to understand whether developers can truly move away from Nvidia's CUDA without sacrificing functionality or performance
• Monitor Foundry Capacity: Track SMIC's quarterly revenue and capacity announcements to gauge whether China's semiconductor manufacturing infrastructure can support a 2.2x increase in AI accelerator production

The broader implication of China's AI chip transition is that U.S. export controls are accelerating the fragmentation of the global AI hardware market. Rather than slowing China's AI development, restrictions on Nvidia and AMD sales are forcing Chinese companies to build domestic alternatives, creating a bifurcated market where China develops its own AI infrastructure independent of American suppliers.

However, a critical wildcard remains: whether China's total AI accelerator market will actually remain at 4 million units or shrink significantly. If supply constraints force prices higher or limit availability, Chinese companies and cloud providers may deploy fewer AI accelerators overall, meaning that while domestic vendors capture 90% of sales, the absolute number of chips deployed could be substantially lower than current projections. This scenario would represent a pyrrhic victory, where Chinese companies gain market share but the overall market contracts due to supply limitations and the absence of cheaper foreign alternatives.