"Smaller models, lightweight frameworks, specialized hardware, and other innovations are bringing AI out of the cloud and into clients, servers, and devices on the edge of the network. With that, the AI industry is entering a "new and potentially much larger phase: AI inference," explains an article on the Morgan Stanley blog. They characterize this phase by widespread AI model adoption throughout consumer and enterprise applications. Running AI on the edge solves many of these issues."
"Amazon recently hiked prices 15% for GPUs primarily used for certain ML training jobs, signalling that cloud AI costs, particularly for centralized training, may be unpredictable. IDC predicts that by 2027, 80% of CIOs will turn to edge services from cloud providers to meet the demands of AI inference. However, the shift won't come without hurdles. Real-time performance demands, the large footprint of AI stacks, and a fragmented edge ecosystem remain top hurdles."
Smaller models, lightweight frameworks, specialized hardware, and other innovations are moving AI from centralized clouds to clients, servers, and edge devices. Edge AI enables reduced latency, lower costs, enhanced security, and greater privacy while enabling real-time decisions at the data source. Rising cloud GPU prices and operational uncertainty are pushing organizations toward edge inference. IDC predicts most CIOs will adopt edge services by 2027. Major hurdles include meeting real-time performance, reducing the large footprint of AI stacks, and navigating a fragmented edge ecosystem. Industrial, automotive, and privacy-sensitive applications are driving rapid growth and adoption of edge AI.
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