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Edge computing can solve specific business problems that demand some combination of in-house computing, high speed, and low latency that cloud-based AI can’t deliver, explained Deepu Talla, NVIDIA VP and GM of Embedded and Edge Computing.

The hardware and architecture that can support edge computing has improved significantly over the past year, including GPUs with Tensor Cores for dedicated AI processing, plus secure, high-performance networking gear. And edge server software is growing more sophisticated as well, such as NVIDIA’s EGX cloud-native software stack, which brings traditional cloud capabilities to the edge of the network. He also pointed to the company’s industry-specific application frameworks such as Metropolis for smart cities, Clara for health care, Jarvis for conversational AI, Isaac for robotics, and Aerial for telecommunications — each supporting forms of AI on NVIDIA GPUs.

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