AI Training Overseas to Access Nvidia Chips, Says

China’s leading technology companies are accelerating their artificial intelligence ambitions by shifting major AI model training operations overseas to access advanced Nvidia chips, according to a recent report by the Financial Times. With U.S.

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export restrictions blocking the sale of high-performance GPUs directly to China, firms such as Alibaba, ByteDance, Tencent, and Baidu are strategically moving their compute-heavy AI training workloads to data centers in regions like Southeast Asia. This move allows them to legally obtain and use Nvidia’s top-tier chips, which remain critical for training cutting-edge large language models and generative AI systems.

The export restrictions have significantly impacted China’s AI roadmap. Since advanced Nvidia processors—such as the A100 and H100—are essential for scaling AI systems efficiently, the inability to purchase them domestically forces Chinese firms to adopt creative workarounds. Offshore data centers provide a legal and operational path forward, enabling companies to continue innovating without violating U.S. sanctions. These data centers lease access to restricted GPUs, giving Chinese AI teams the computing power needed to compete globally.

This overseas shift highlights a growing divide between China’s domestic AI infrastructure and the international compute ecosystem. While training occurs abroad, deployment, fine-tuning, and product integration typically return to China—where companies rely on local hardware solutions or earlier-generation chips. This creates a dual strategy: maximize global compute access while ramping up investment in homegrown semiconductor development. Firms like Huawei are rapidly advancing domestic AI chip capabilities, but these solutions still lag behind Nvidia’s best-performing processors.

The FT report also noted that the shift may accelerate innovation in Southeast Asian data-center hubs. Regions like Singapore and Malaysia are becoming attractive alternatives for compute-intensive AI research due to their political neutrality, access to cloud infrastructure, and flexible technology partnerships. This is transforming the geography of global AI development and reshaping the balance of power in the tech competition between the U.S. and China.

Despite hurdles, China’s tech giants remain determined to sustain rapid progress in AI. By relocating training operations offshore, they ensure continuity, global competitiveness, and faster innovation cycles. This approach also signals to investors and analysts that China’s AI momentum remains strong despite geopolitical pressures.

The long-term picture is clear: China will continue to diversify its compute sources—offshore and domestic—while pushing aggressively toward semiconductor independence. The overseas training strategy is not just a workaround; it is a strategic adaptation that will influence future global AI infrastructure and geopolitical technology dynamics.

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