FEATURE : EDGE COMPUTING
Low latency and costs Like other digital services , AI applications require low latency to deliver a responsive user experience . This was initially driven by the needs of the gaming , e-commerce , finance and entertainment sectors . AI follows this same trend , benefitting from secure , high-performance global networks and Edge compute resources .
While the centralised cloud provides immense compute power for training , it is now struggling to compete against other providers when it comes to privacy , low latency and cost efficiency . CIOs would benefit from AI inference distribution that can costeffectively serve the less intensive inference workloads to end-users on an on-demand , pay-as-you-go basis . These are calculated against the GPUs needed , allowing businesses to start with basic GPUs and scale resources elastically based on usage .
Use cases and challenges Enterprises are still understanding AI ’ s potential use cases across industries such as automated systems , robotics , HR and customer service chatbots . But they need to weigh factors like data control , compliance , skills and budgets .
Large Language Models like ChatGPT offer powerful but generalised AI capabilities . For
CIOS WOULD BENEFIT FROM AI INFERENCE DISTRIBUTION THAT CAN COST-EFFECTIVELY SERVE THE LESS INTENSIVE INFERENCE WORKLOADS TO END-USERS ON AN ON-DEMAND , PAY-AS- YOU-GO BASIS .
Andre Reitenbach , CEO , Gcore
40 INTELLIGENTCIO EUROPE www . intelligentcio . com