AI inference in practice: choosing the right edge
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Inferencing is the real-time decision-making of an AI model. As AI adoption grows, inferencing will accelerate, raising questions about workload processing and business benefits. As outlined in "Distributed inference: how AI can turbocharge the edge", enterprises need to know that where inferencing happens – whether at the user edge (device and enterprise) or network edge (far edge, near edge and telco private cloud) – will have major implications for application performance, data sovereignty, resilience and energy efficiency.
This research forms part of a series illustrating the impact of AI inference, with each report focusing on a distinct edge location and featuring an example company. This analysis examines how running AI workloads on the edge can deliver improved outcomes, with Aible the featured company.
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As AI adoption grows, inferencing will accelerate, raising the question of where workloads will be processed and how they translate into business benefits. This analysis examines AI on the near edge in distributed telco data centres, with Kinetica highlighted as an example.
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MWC Shanghai is in the books for another year, having attracted 45,000 visitors (from 12,500 companies), along with 400 exhibitors and partner groups. The numbers were up from the 2024 event by 13% and 92% respectively. This analysis highlights the key takeaways and implications from meetings, summits and announcements at the event – and in particular, whether the progress seen in China can be mapped to other regions.
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