The NVIDIA Grace Hopper Superchip Architecture is the first true heterogeneous accelerated platform for high-performance computing (HPC) and AI workloads. It accelerates applications with the strengths of both GPUs and CPUs while providing the simplest and most productive distributed heterogeneous programming model to date. Scientists and engineers can focus on solving the world’s most important…
]]>Today during the 2022 NVIDIA GTC Keynote address, NVIDIA CEO Jensen Huang introduced the new NVIDIA H100 Tensor Core GPU based on the new NVIDIA Hopper GPU architecture. This post gives you a look inside the new H100 GPU and describes important new features of NVIDIA Hopper architecture GPUs. The NVIDIA H100 Tensor Core GPU is our ninth-generation data center GPU designed to deliver an…
]]>Our most popular question is “What can I do to get great GPU performance for deep learning?” We’ve recently published a detailed Deep Learning Performance Guide to help answer this question. The guide explains how GPUs process data and gives tips on how to design networks for better performance. We also take a close look at Tensor Core optimization to help improve performance. This post takes a…
]]>At 45 images/s/W, Jetson TX1 is super efficient at deep learning inference. Read the whitepaper. Deep learning is revolutionizing many areas of machine perception, with the potential to impact the everyday experience of people everywhere. On a high level, working with deep neural networks is a two-stage process: First, a neural network is trained: its parameters are determined using labeled…
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