Huawei unveiled All Intelligence Strategy to support industries 

By Morkporkpor Anku (Courtesy: Huawei Technologies Ghana) 

Shanghai, Sept. 24, GNA- Madam Meng, Huawei’s Deputy Chairwoman, Rotating Chairwoman, and CFO, has unveiled the company’s All Intelligence Strategy to enhance support for industries. 

She described the company’s ongoing efforts to dive deep into foundational AI technologies and build a solid computing backbone for China and for the world to support a vast range of AI models and applications for all industries. 

Madam Sabrina Meng, Huawei’s Deputy Chairwoman, Rotating Chairwoman, and CFO, wss speaking at Huawei Connect 2023, which brought together business leaders, tech experts, partners, developers, and industry stakeholders from around the world to explore new opportunities for an intelligent future. 

The two-day event was on the theme: “accelerating industry intelligence.” 

Huawei also released a reference architecture for driving intelligent transformation at this year’s event, as well as a number of related products and solutions.  

This reference architecture is included in the company’s new white paper, Accelerating Intelligent Transformation, which offers practical advice and references to help industries make the most of intelligence. 

She said for the past two decades, Huawei had worked with the industry to drive information and communications technology forward.  

First with its All IP strategy to support informatisation, and then with its All Cloud strategy to support digitalization.  

She said as artificial intelligence gained steam, and its impact on industry continued to grow, Huawei’s All Intelligence strategy was designed to help all industries make the most of new strategic opportunities presented by AI. 

She said key to this strategy was providing the massive amounts of computing power needed to train foundation models for different industries. 

“Huawei is committed to building a solid computing backbone for China – and another option for the world,” Madam Meng said. 

The Deputy Chairwoman said “We will keep strengthening the synergy between hardware, software, chips, edge, devices, and cloud to provide fertile ground for a thriving ecosystem. Our end goal is to help meet the diverse AI computing needs of different industries.” 

Madam Meng said going forward, Huawei would dive into the product and tech domains where they excel, and work closely with customers, partners, developers, and other stakeholders to provide cutting-edge, easy-to-use industry solutions.  

“By working together, we can help promote greater digital security and trustworthiness, and accelerate intelligence across all industries,” she added. 

She said, “Competence breeds confidence and the future is one we build together and to succeed in  

the intelligent future to come: There is strength in solidarity. And victory through grit.” 

Mt David Wang, Huawei’s Executive Director of the Board, Chairman of the ICT Infrastructure Managing Board, announced the launch of Huawei’s new Atlas 900 SuperCluster. 

This new AI computing cluster, the latest offering in Huawei’s Ascend series of computing products, makes use of a brand-new architecture that was optimized for training massive AI foundation models with over one trillion parameters. 

He said the Atlas 900 SuperCluster comes packed with Huawei’s state-of-the-art Xinghe Network CloudEngine XH16800 switch. 

He said with high-density 800GE ports, the SuperCluster’s two-layer switching network could connect up to 2,250 nodes per cluster – equivalent to 18,000 NPUs without oversubscription. 

Mr Wang said the cluster’s innovative super node architecture greatly boosted its overall computing power and took the speed and efficiency of foundation model training to an entirely new level. 

He said Huawei had leveraged its strengths in computing, storage, network, and energy to systematically improve system reliability at the component, node, cluster, and service levels. 

“System reliability is incredibly important for training massive foundation models, and this approach has effectively extended the cluster’s ability to support continuous model training from several days to a month or more,” he said 

GNA