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Home / Technology / China Unleashes 1,243-Mile AI Computing Power Pool

China Unleashes 1,243-Mile AI Computing Power Pool

12 Dec

•

Summary

  • China launched a 1,243-mile distributed AI computing pool.
  • The network connects 40 cities, achieving 98% efficiency.
  • It dramatically speeds up data transfer for AI and research.
China Unleashes 1,243-Mile AI Computing Power Pool

As 2025 concludes, China has unveiled a groundbreaking technological achievement: a vast distributed AI computing power pool. This 1,243-mile network, known as the Future Network Test Facility (FNTF), unifies the nation's leading computing centers, operating as a single, cohesive system.

The FNTF is a high-speed optical network extending across 40 cities, measuring an impressive 34,175 miles. Project director Liu Yunjie highlighted its efficiency, stating it reaches 98% of a single center's capability. This makes it revolutionary for real-time demanding tasks, including large-scale AI model training, advanced telemedicine, and the industrial internet.

Central to China's 'East Data, West Computing' project, the FNTF connects data-rich western regions with high-demand eastern hubs. It operates 24/7, supporting numerous networks and trials. Early tests demonstrated rapid data transmission, completing in hours what would normally take hundreds of days, signaling a significant leap in AI development and research capabilities.

Disclaimer: This story has been auto-aggregated and auto-summarised by a computer program. This story has not been edited or created by the Feedzop team.
The FNTF is a 1,243-mile distributed AI computing power pool connecting 40 cities in China to unify its top computing centers.
The network achieves 98% of a single computing center's efficiency while connecting distant hubs, ideal for real-time applications.
It's an initiative to connect western China's data resources with eastern China's AI development demands, with FNTF as its centerpiece.

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Technologyside-arrowChinaside-arrowArtificial Intelligence (AI)side-arrow

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