CIO OPINION
By running AI workloads where data exists, organisations can remain compliant while also avoiding duplicate transfers between systems.
Tony Bartlett, Director of Data Centre Compute, Dell Technologies South Africa
Why your inferencing is more likely to be onpremises than in cloud
As Large Language Models continue to downsize while maintaining high performance, more models are running on portable computers such as AI PCs or workstations on-premises and at the edge. These factors underscore why 73 % of organisations prefer to self-deploy LLMs based on infrastructure operating at data centres, devices, edge locations, explains Tony Bartlett at Dell Technologies South Africa.
AI is migrating from computers and phones to robots, self-driving cars and any digital space imaginable. Even NVIDIA CEO Jensen Huang called this out at the company’ s recent GPU Technology Conference.
Generative AI is growing rapidly, with 75 % of knowledge workers using it to create content like sales collateral or automate coding, says Accenture. According to a survey by Boston Consulting Group, 90 % of South African employees using Generative AI for work believe it has saved them time, with another 42 % reporting confidence in its impact on their work. Around 87 % of local respondents reported an improved quality of work, saving them time to focus on strategic work and reducing time spent on administrative tasks.
The key to effective AI outcomes is a centralised AI strategy that considers various technical and operational factors. Different use cases will require different models and processes, as well as access to high-quality data to help maximise productivity.
Increased productivity is not the only consideration. As organisations put their AI projects through their paces, they must also respect their budgets and protect their data. And as with all emerging technologies, AI presents implementation hurdles.
38 INTELLIGENTCIO AFRICA www. intelligentcio. com