Xu Ziyang delivered keynote speeches at both the “AI First” session and the GTI Summit during MWC Shanghai 2024, focusing on “Ingenuity for Solid Foundation, Openness for Win-Win” and “Computing and Network Evolution Towards an Intelligent Future”.
ZTE Corporation , a global leading provider of integrated information and communication technology solutions, announced today that the company’s CEO, Xu Ziyang, has delivered keynote speeches at both the “AI First” session and the GTI Summit themed “5G-A×AI” at MWC Shanghai 2024. Mr. Xu shared ZTE ’s practices and innovations in intelligent digitalization amidst the AI wave.
This establishes a robust foundation through technical expertise and fosters innovation through collaborative partnerships, aimed at empowering various industries in their digital transformation. This accelerates the convergence of digital and physical realms, enhances production and transaction efficiency, and establishes agile, resilient organizations prepared for an uncertain future.
Good day, everyone. It’s an honor to be invited to speak here at the MWC Shanghai. Today, I’d like to share ZTE’s insights and practices under the theme of “Embracing Changes and Promoting Intelligent Evolution This is equivalent to USD 10.7 trillion and accounts for almost 70% of the global economic impact. Apparently, AI will bring unprecedented business opportunities in various sectors such as retail, financial services, and healthcare.
Finally, accelerating standardization through openness and decoupling will help build a thriving industrial and commercial ecosystem.To begin with, we emphasize computing and network evolution to build a highly-efficient foundation. This solution can effectively address the inflexibility and low bandwidth utilization of the mesh interconnect architecture.
In addition, optical I/O brings revolutionary improvement in bandwidth density, power efficiency, and latency.In this regard, for example, ZTE has been fully collaborating with China Mobile to foster a robust industry ecosystem of Global Scheduling Ethernet , aiming to build a non-blocking, high-bandwidth, and ultra-low-latency network for the next-gen intelligent computing centers.
Meanwhile, for the building of domain-specific models based on a foundation model, expertise in data governance, incremental training, and related engineering experiences and toolsets are also crucial. During this circulation of mutual enhancement, a data flywheel is established. This will further accelerate the improvement and monetization of AI capabilities, which then turn intopartnerships with high-value industries and angel customers.
The software supports heterogeneous resource management, training and inference job scheduling, and heterogeneous collective communication. Compatible with GPUs from multiple manufacturers, the software enables a high-performance and reliable runtime environment for models. As a Chinese saying goes, “A single flower does not make spring, while one hundred flowers in full blossom bring spring to a garden.” Similarly, a full-stack, open intelligent computing solution contributes to an open technology ecosystem and a win-win business ecosystem, which will in turn advance the healthy development of intelligent computing.
In terms of digital infrastructure, ZTE provides a full series of computing, storage, network, and data center products and solutions, to fulfill various construction needs for intelligent computing centers from the core to the edge. Compatible with the RDMA protocol for unified intra-node and inter-node communication, OLink focuses on promoting intra-node communication by shifting from mesh interconnects to switch-based interconnects.
Meanwhile, ZTE AIS, a training and inference platform, can be applied to data processing and development of large models, providing full-stack engineering toolkits and engines. Taking seamless migration as an example, availability can be achieved within 5 days, and optimal performance within 15 days.
As for inference efficiency, by quantizing weights to INT4 and KV cache to FP8, inference resources are saved threefold without compromising model accuracy.
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