VentureBeat November 9, 2024
In today’s fast-paced digital landscape, businesses relying on AI face new challenges: latency, memory usage and compute power costs to run an AI model. As AI advances rapidly, the models powering these innovations have grown increasingly complex and resource-intensive. While these large models have achieved remarkable performance across various tasks, they are often accompanied by significant computational and memory requirements.
For real-time AI applications like threat detection, fraud detection, biometric airplane boarding and many others, delivering fast, accurate results becomes paramount. The real motivation for businesses to speed up AI implementations comes not only from simply saving on infrastructure and compute costs, but also from achieving higher operational efficiency, faster response times and seamless user experiences, which translates into tangible...
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