Overcoming Growth Barriers with AI-Ready Data Infrastructure

AI technologies are a disruptive force that push organizations to evolve in order to stay ahead. To be AI-ready, enterprises must get data-ready. This cannot be achieved without a robust data infrastructure as stated by Huawei's Dr. Peter Zhou,who in a recent speech explained the importance of AI in business success.

The importance of AI-ready data infrastructure has been covered in our previous blog,Brilliance in Resilience: How Al-Ready Infrastructure is Shaping Tomorrow's World, which explains the characteristics, business resilience, and other aspects that boost operations.

Here, we explore the real-world advantages of deploying AI-powered infrastructure.

AI services firing on all cylinders

Large AI models have made waves throughout the world. From mainstream models like ChatGPT and Sora, to industry-specific foundation models, AI can empower industry applications with speed and scale.

Behind this boom of AI is an ever-skyrocketing amount of model parameters witnessed by AI service companies. Especially in the development phase of single-mode large models, the number of parameters reaches 100 billion. Many AI companies simply cannot handle this growth with current infrastructure solutions. Conventional practices like divided, independent clusters and unreliable external storage are ineffective in handling and making use of huge data volumes. Moreover, multiple clusters do not improve aggregation performance.

Companies are struggling with the time-consuming processes of mass data preparation, training interruptions, and checkpoint loading: AI-ready infrastructure offers a compelling solution to problems like these.

Take the AI data lake base that disaggregates computing from storage as an example. Such a solution enables separate resources for compute and storage. On the storage side, multiple sets of AI storage systems provide reliable and efficient access to PB-scale available capacity. This also enables fast training resumption and solid single-cluster reliability with functions such as sub-health management and high-ratio EC.

Unlocking AI's potential in finance

Around the world, financial institutions are using digital tech to provide faster, more convenient services. AI models can be deployed on big data platforms, but the surge in scenario-specific models and data volumes is not supported by the legacy storage foundation, a typical bottleneck that causes failures during high IOPS peak periods and impacts normal operations. In many cases, a single site failure can cause services and even whole businesses to go offline, something that is compounded when there is no multi-site, multi-active protection in place.

High-concurrency workloads, like those in finance, need high-performance storage systems that support multi-active sites. Huawei OceanStor Pacific Scale-Out Storage series is designed for this. With a parallel file system, the storage solution provides high OPS and high bandwidth to process hybrid I/Os. Files are retrieved from hundreds of billions of objects in a flash. With automatic data tiering policy in a cluster, the overall TCO is slashed and, with multiple sites in place, operations can continue uninterrupted even if one or two sites go offline, ensuring solid reliability.

Carriers are moving to AI computing centers

Intelligent computing platforms provide an abundant source of computing, storage, and network resources. Many carriers use new AI platforms to boost model training and inference tasks, in order to make up for the deficiencies in insufficient storage bandwidth and unstable performance. Conventional platforms, for example, need 10 minutes to load a single checkpoint during large model training, while slow GPU wait times result in low computing power utilization. A fault on a local disk requires data reconstruction that could take several hours, consuming CPU resources and leading to unstable upper-layer services.

Carriers require professional-grade storage that can collaborate with their file systems to realize a reliable architecture alongside ultra-high bandwidth, active-active hardware, comprehensive disk sub-health management, and global data reconstruction. Robust storage enables better energy efficiency while supporting always-on services. Huawei OceanDisk Smart Disk Enclosure is one such solution, offering wide compatibility to support seamless interconnection options for carriers to expand their operations.

Why Huawei

AI is forcing industries to evolve. It offers potential for innovation and unprecedented opportunities, which is why enterprises from finance, education, healthcare, and other sectors are using AI to boost operations. AI-ready data infrastructure has demonstrated its irreplaceable value in improving the operational efficiency and business performance, be it with faster training or the resilience of multi-site protection.

For two decades, Huawei has invested heavily in developing best-in-class data infrastructure, recognizing its importance to industry development. Huawei's portfolio of high-performance and high-reliability storage solutions, such as OceanStor AI storage and OceanStor Pacific scale-out storage, offer the competitive edge to help enterprises stay ahead in the data awakening era.

Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.