ZTE sets sights on intelligent AI infrastructure and networks at MWC Shanghai 2025

ZTE Presents New AI Infrastructure Vision at MWC Shanghai 2025

ZTE has outlined a clear path for advancing AI infrastructure during MWC Shanghai 2025, focusing on AI-native networks, smarter data models, and comprehensive edge-to-core AI deployment. This approach targets efficient, sustainable, and scalable solutions to meet rising AI compute demands across industries.

Key Highlights of ZTE’s AI Infrastructure Strategy

• AI-native networks: Redesigning telecom infrastructure with embedded AI intelligence, modular data centers, and AI-driven energy management systems.
• Enhanced models and data quality: Utilizing advanced training methods and tools to improve AI model accuracy and real-world effectiveness.
• Edge-to-core AI deployment: Integrating AI in consumer, home, and industrial applications with over 100 solutions across 18 verticals through the “AI for All” initiative.

At the keynote, ZTE’s Chief Development Officer Cui Li emphasized the importance of foundational infrastructure in the growth of large AI models. She stressed that the future of AI will depend on efficient infrastructure, optimized algorithms, and practical applications.

Building Efficient and Sustainable AI Infrastructure

ZTE is investing heavily in modular, prefabricated data centers and AI-based power management to reduce energy consumption and cooling requirements by more than 10%. These advances aim to provide flexible capacity that supports the increasing computational needs of AI applications without compromising sustainability.

Transitioning to “AI-native” networks means moving away from traditional telecom architectures toward heterogeneous computing platforms with built-in AI capabilities. This shift transforms AI from a mere support tool into an autonomous agent influencing network operations and decision-making.

Data quality and security remain critical. Cui Li compared data to fertile soil—its volume, purity, and security determine how effectively AI can develop and perform.

Expanding AI Applications Across Sectors

ZTE is applying full-stack intelligent computing solutions across various domains, including smartphones, PCs, smart homes, and industrial environments. The goal is to create a seamless loop of perception, computing, interaction, and execution within home settings.

In industry, ZTE’s Nebula AI models have helped shorten production cycles and improve energy efficiency, with deployments in over 100 projects spanning 18 vertical sectors.

Transforming Network Architecture and Operations

ZTE is pushing an architectural shift from traditional dedicated hardware networks to AI-native heterogeneous computing platforms. This change enhances efficiency and enables AI to act as an autonomous decision-maker rather than just an assistant.

This transformation is expected to reshape network operations, service models, and create new business opportunities for intelligent network applications.

MWC Shanghai Demonstrations: Radio Access Network Innovations

During MWC Shanghai, ZTE showcased products aimed at helping operators deploy 5G networks with full-band coverage, simplified site setups, improved user experiences, and deep AI integration.

Responding to increasing uplink bandwidth demands from AI terminals, autonomous vehicles, and embodied intelligence applications, ZTE introduced uplink 3-carrier aggregation (3CC CA) technology. This solution maximizes spectrum use, achieving uplink speeds over 865Mbps in commercial mid-band trials with China Mobile.

ZTE’s AIR RAN solution integrates heterogeneous computing with an AI-powered baseband unit (BBU) computing board called AIREngine. This digital upgrade spans antennas, transmission, and power systems, converting legacy radio access networks (RAN) into intelligent infrastructures deeply integrated with AI.

For those interested in expanding their AI skills and understanding how AI integrates with modern infrastructure, exploring up-to-date AI courses can provide practical insights and technical knowledge.