U N C O V E R I N G T H E L A Y E R S

However , processing AI workloads in data centres presents new challenges , primarily due to the unique demands AI traffic imposes , compared to traditional data centres .

The cluster of computers and especially GPU processors that constitute AI , for example , within the data centre , require high speeds for data processing . Accordingly , bandwidth must provide robust and consistent capacities , reaching speeds of 800Gbps and beyond . Moreover , this capacity ' s scalability is vital to meet varying business demands . It involves efficiently leveraging data centre resources to reduce costs , ensure consistent performance , prevent overloads , allocate

Scalability also enables data centres to swiftly adapt to evolving business requirements , enhancing redundancy and maintenance capabilities with minimal disruption to operations .

Ensuring the right network environment for AI

Running AI workloads in data centres requires the right network environment , for example , processing AI traffic via the familiar Ethernet protocol , instead of the more niche InfiniBand .

Utilising intent-based networking in addition to AIOps will make data centres far more suitable for processing AI workloads . Intent-based networking architecture allows companies to describe business goals , after which the data centre networking environment automatically converts them to the right configurations , based on network information , analytics and orchestration .

These configurations are continuously validated via closed-loop validation to check whether they still meet the objectives and are automatically adjusted as necessary .

Intent-based networking ensures data centres maintain the correct network configuration , optimising them specifically for handling AI workloads . This automated approach not only relieves administrators of manual tasks but also consistently enhances user experiences to meet desired standards .

Data centre operators using intent-based networking plus AIOps to radically simplify and improve the design , deployment and ongoing operations of infrastructure will increasingly differentiate themselves from their competitors .

Deploying a comprehensive AInative networking platform

AI will continue to enjoy widespread utilisation across diverse organisations , increasingly shaping their applications and data processing . Data centres will play a pivotal role in this deployment .

A network environment that is foundationally optimised for AI ensures that AI workloads can be processed efficiently . AI-native networking platform enables companies and administrators to manage work environments via AIOps from a single environment and optimally process AI workloads within controlled conditions .

This in turn allows enterprises , administrators and end-users to focus their efforts on key business objectives without having to worry about the required network infrastructure and architecture in place . �

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