Huawei integrates storage and AI into a five-layer infrastructure

Processed tokens and AI agents in production are increasing exponentially, creating a need for corporate infrastructure that can support data and AI opportunities. Huawei states that a five-layer AI stack is required to deploy artificial intelligence securely and quickly for heavy workloads. The focus shifts toward infrastructure solutions centered on data so more intelligence can be integrated into business environments. Token volume is projected to rise from about 6 billion tokens per minute worldwide to 15 billion, while active AI agents are estimated to grow from about 2.2 billion to nearly 30 million. In healthcare, a hospital deployed three digital pathology AI models trained on one million images and knowledge from 300 medical textbooks, reducing pathology report generation time from 40 minutes to 15 seconds.

"The number of processed tokens and agents in production is growing exponentially. Corporate infrastructure must be built to support this, or else the opportunities offered by data and AI will remain underutilized. According to Huawei, a five-layer AI stack is required to deploy artificial intelligence securely and quickly for heavy workloads."

"Last year, approximately 6 billion tokens per minute were processed worldwide. This year, that number is expected to grow to 15 billion. Meanwhile, IDC figures estimate nearly 30 million active AI agents at the end of last year, up from an estimated 2.2 billion in four years."

"Tokens, in his words, are becoming as indispensable as air and water. But tokens alone don't tell the whole story. The question is what infrastructure is needed to make those tokens meaningful for business processes, so that AI becomes truly valuable."

"One of China's largest hospitals deployed three AI models for digital pathology. One was used to detect cancer cells, another to emulate microscopic examination, and the last to handle patient communication. The models were trained on a million digital pancreatic images and absorbed knowledge from 300 medical textbooks, processed via 16 GPU cards. By deploying AI here, the time to generate a pathology report dropped from 40 minutes to 15 seconds."

Read at Techzine Global