Nvidia, the undisputed leader in the AI computing market, is currently navigating different balancing acts to maintain its leadership. On one side, it’s trying to reduce reliance on cloud hyperscalers like AWS and Google Cloud, which account for over 50% of its data center revenue. On the other hand, it is doubling down on China’s $50 billion AI chip market despite the tightening of the USA export restrictions.
Strategy 1: Rethinking Hyperscaler Dependence
In the past, Nvidia has relied heavily on a handful of giant cloud providers (hyperscalers) like Amazon Web Services, Google Cloud, and Microsoft Azure for its business. The hyperscalers accounted for more than half of Nvidia’s data center revenue. However, these companies are increasingly developing their custom AI chips (ASICs), which could reduce their dependence on Nvidia’s GPUs and threaten Nvidia’s future market share. Thus, considering that fact, Nvidia is looking forward to diversifying its customer base to include national governments, large corporations, and emerging cloud providers (such as CoreWeave, Nebius, Crusoe, and Lambda), which will help the company to reduce its exposure to this risk.
Pros:
- This approach opens up multi-billion-dollar opportunities outside the traditional hyperscaler segment and helps Nvidia maintain strong growth
- It also allows Nvidia to tap into rapidly growing regions and industries that are building their AI infrastructure
- By broadening access to its GPUs and services, Nvidia is addressing frustrations among startups, enterprises, and researchers over limited GPU availability and vendor lock-in
- Nvidia’s strategy could accelerate the decentralization of AI infrastructure, with more countries and companies building their own “sovereign AI” capabilities. This may drive further demand for customizable, high-performance AI hardware and software from other countries
Cons:
- Hyperscalers still contribute a significant amount to Nvidia’s annual sales, and a sudden shift could destabilize short-term growth
- AMD, Intel, and even Huawei are vying for market share in AI hardware and software
Strategy 2: Localization and customization for the China Market
The USA and China tech tensions and export controls have limited Nvidia’s access to the lucrative Chinese market, which represented about 14% of its revenue in 2024. In response to the current restrictions and its determination to maintain its foothold in China, Nvidia has announced that it will open a new R&D facility in Shanghai, which will focus on tailoring solutions for Chinese clients, researching the specific technical needs of the local market, and ensuring compliance with the USA regulations. It is also anticipated that the company is going to start the mass production of its downgraded Blackwell-series GPU for China, priced at $6,500-$8,000, which was more than $10,000 for the H20 previously. The new production will avoid restricted technologies like CoWoS packaging and high-bandwidth memory, complying with bandwidth caps of 1.7 TB/s.
Pros:
- The Shanghai R&D center allows Nvidia to tailor products for Chinese clients and maintain strong relationships with local partners and authorities
- Investing heavily in R&D and local talent, Nvidia can accelerate product development, adapt to regional needs, and keep its technology at the forefront of the AI race
- The move will also reassure stakeholders that it remains committed to China, even as it navigates a challenging regulatory environment and global trade tensions
- Lower product cost will be appealing to budget-conscious Chinese firms
Cons:
- The USA policies are still volatile, and further restrictions could render Nvidia’s China-specific chips obsolete
- Weaker product specifications may push customers toward domestic alternatives like Huawei, which already holds 50% of China’s AI chip market
- The H20 ban forced Nvidia to write off $5.5 billion in inventory, and a repeat could harm margins
A High-Stakes Gamble with Moderate Success Likely
Nvidia’s move beyond hyperscalers can be counted as a success, as it is a strategic response to the risk of losing market share to in-house chips developed by Big Tech. By broadening its customer base and offering full-stack AI solutions, Nvidia intends to strengthen its market position, ensure continued growth, and make itself more resilient in the face of industry shifts.
Moreover, Nvidia’s CUDA platform and AI Enterprise software create dependency for its existing customers, making it harder for them to switch to competitors or substitutes. Hence, it will retain hyperscalers also.
But Nvidia’s attempt to go local in the Chinese market may provide it with limited gain in the long term. While Nvidia may claw back some revenue, Huawei’s ascendancy and the USA policy shifts will cap its growth. Further, in China, hyperscalers like Alibaba and Tencent may explore partnerships with domestic chipmakers, over the downgrade in technology and quality of chips by Nvidia.
Expected Future Strategies
- Nvidia may continue to expand R&D and partnerships in other countries, such as India, the Middle East, and Europe, to deepen its foothold and adapt to regulatory environments
- Nvidia is expected to invest more in AI platforms, developer tools, and cloud-based services, in order to become the “operating system” for AI globally
- Nvidia could pursue joint ventures with local tech giants or governments to co-develop AI solutions tailored to regional needs
- As AI adoption spreads, Nvidia may also introduce industry-specific chips and solutions, broadening its addressable market even further