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Achieving Faster AI with NVIDIA GPUs and TensorRT

In this last year, GPU deep learning has gone from a hot research topic to a large-scale deployment challenge in major data centers. That’s because deep learning is extraordinarily effective and now powers every application, from speech recognition to self-driving cars, from language translation to better search. Power efficiency and processing power make GPUs the right fit for deep learning and inference from the edge to the data center.

Watch this replay to learn:
  1. How neural nets, frameworks, and GPU architectures have changed significantly in the last year as well, allowing better solutions to be created more quickly and in more places, moving from niche applications to the mainstream
  2. How to save on cost, while achieving better AI performance, efficiency, and responsiveness with NVIDIA’s deep learning platforms
  3. How to unleash the full potential of NVIDIA GPUs with NVIDIA TensorRT
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Presented By
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Rob Ober
Tesla Chief Platform Architect

Rob is responsible for the GPU platform architecture in hyperscales, enabling their rapid adoption of this technology at NVIDIA. Previously, Rob was Senior Fellow of Enterprise Technology at SanDisk via the FusionlO acquisition. Prior roles: Chief Architect and Corporate Fellow in CTO and Corporate Strategy Offices at LSI; Fellow in the Office of the CTO at AMD; Chief Architect at Infineon Technologies; Manager of Newton Technologies at Apple Computer, and designer of supercomputers and mainframes. Rob has more than 40 international patents and 18 disclosures in processor architecture, storage systems, SSDs, networks, power management, and mobile devices.
 
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