NVIDIA WEBINAR
Sub brand
Date: June 26, 2019
Time: 10:00am – 11:00am PT
Duration: 1 hour
Time: 10:00am – 11:00am PT
Duration: 1 hour
Time is a commodity, but especially for today’s AI researcher. Often working against the clock to make submission deadlines, researchers know that “more time” could determine whether you create the next, great breakthrough. NVIDIA® TITAN RTX, built with NVIDIA Turing™ architecture, trains models faster than ever, delivering 130 TFLOPS of AI performance, 4x faster than the TITAN Xp. This webinar will explore benchmarks, use cases, and look at how researchers can experiment with larger neural networks and datasets all on GPU memory.
In this webinar, you’ll learn about:- performance for training neural networks and processing large data sets
- tools and libraries NVIDIA® TITAN RTX supports, including NVIDIA’s CUDA-X AI SDK, cuDNN, TensorRT and more than 15 other libraries
- compatibility with popular deep learning frameworks and NVIDIA GPU Cloud (NGC)
- research powered by TITAN RTX
WEBINAR REGISTRATION
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DGX Station Whitepaper
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
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Speaker
Jesse Clayton
Senior Product Manager, NVIDIAJesse Clayton is the Sr. Product Manager for Titan He has more than 20 years of experience in technology, spanning software, GPU computing, embedded systems, and aeronautics. His current focus is on building accelerated computing solutions for AI researchers, data scientists and content creators everywhere. Prior to joining NVIDIA in 2005, he conducted NASA-funded research on aviation systems and algorithms. Clayton holds a Bachelor of Science degree in electrical and computer engineering from the University of Colorado, Boulder.
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