With the rapid growth of digital transformation, many enterprise data centers are now focusing on disaster recovery (DR). The need for enterprise survival, regulatory compliance, and protection against natural disasters are all driving factors behind the upgrade of enterprise data centers for DR purposes.

In light of this situation, Huawei has developed the Ultra-Resilient Multi-DC Network Solution, which leverages high-performance CloudEngine series switches and the iMaster NCE intelligent management and control platform to achieve easy interconnection, intelligent O&M, high reliability, and evolvability.

This solution is best suited to industries such as government, finance, education, and manufacturing, empowering customers with best-in-class fault-tolerant and always-on services.

As digital transformation sweeps across all industries, enterprises are presented with new opportunities for revenue growth. However, with these opportunities come challenges, such as difficulties in ensuring user experience, a lack of operational tools, and complex management in operations. These issues can hinder the digital transformation of enterprise campuses.

Huawei is leading the industry by introducing the concept of the campus network digital map. This map serves as the digital foundation of the campus network, creating a four-dimensional digital space that helps deliver ultimate user experience. The campus network digital map enables comprehensive digitalization of campus networks and empowers enterprises to capitalize on the opportunities it presents.

Large AI model parameters have grown exponentially in scale, from the hundreds of billions to trillions. Training sets have also evolved from unimodal to multi-modal (images, videos, etc.) paradigms, resulting in a data explosion that has increased volumes by thousands, and brought unprecedented challenges.
First, poor loading performance among massive numbers of small files has undermined training efficiency.
Second, large model parameters are frequently being optimized, which has led to unstable training platforms, with service interruptions occurring every two days on average, and frequent, time-consuming read and write of TB-level checkpoints.
Third, training 100-billion-parameter models requires massive GPU resources, since individual GPUs are not efficient enough, and ensuring millisecond-level real-time inference for conversational applications is quite challenging.

OceanStor A800 delivers a staggering 24 million IOPS and 500 GB/s bandwidth within a single controller enclosure, serving as the high-performance AI storage solution. With four times the performance of the next runner-up, a single storage system is all you need for small file loading of training sets and high-bandwidth-based resumable training. You'll also benefit from access to a leading intrinsic vector knowledge repository, which supports large model inference applications with over 15,000+ QPS, achieving accelerated vector retrieval and millisecond-level inference response.