October 30, 2023
Nvidia, this month, unexpectedly released an updated GPU roadmap with new products every year. The new GPUs for 2024-2026 came despite customers lining u Read more…
August 21, 2023
Creating good scientific software is hard. Oftentimes, software developed to solve a specific problem in a particular area of research. Many projects often work Read more…
July 12, 2023
Worldwide revenue for the public cloud services market totaled $545.8 billion in 2022, an increase of 22.9% over 2021, according to new data from the IDC Worldw Read more…
May 29, 2023
Nvidia launched a new Ethernet-based networking platform – the Nvidia Spectrum-X – that targets generative AI workloads. Based on tight coupling of the Nvi Read more…
May 23, 2023
Jay Lofstead from Sandia National Laboratories and Jakob Luettgau from the University of Tennessee gave a highly audience interactive session on Ethics in AI an Read more…
April 20, 2023
Famously, a team of researchers from the University of Massachusetts, Amherst, concluded in 2019 that training a single large AI model could emit five times the Read more…
April 12, 2023
Generative AI is taking the tech world – and the broader world – by storm, but relatively little word has come out of the major supercomputer centers amid t Read more…
April 5, 2023
MLCommons today released the latest MLPerf Inferencing (v3.0) results for the datacenter and edge. While Nvidia continues to dominate the results – topping al Read more…
Making the Most of Today’s Cloud-First Approach to Running HPC and AI Workloads With Penguin Scyld Cloud Central™
Bursting to cloud has long been used to complement on-premises HPC capacity to meet variable compute demands. But in today’s age of cloud, many workloads start on the cloud with little IT or corporate oversight. What is needed is a way to operationalize the use of these cloud resources so that users get the compute power they need when they need it, but with constraints that take costs and the efficient use of existing compute power into account. Download this special report to learn more about this topic.
Data center infrastructure running AI and HPC workloads requires powerful microprocessor chips and the use of CPUs, GPUs, and acceleration chips to carry out compute intensive tasks. AI and HPC processing generate excessive heat which results in higher data center power consumption and additional data center costs.
Data centers traditionally use air cooling solutions including heatsinks and fans that may not be able to reduce energy consumption while maintaining infrastructure performance for AI and HPC workloads. Liquid cooled systems will be increasingly replacing air cooled solutions for data centers running HPC and AI workloads to meet heat and performance needs.
QCT worked with Intel to develop the QCT QoolRack, a rack-level direct-to-chip cooling solution which meets data center needs with impressive cooling power savings per rack over air cooled solutions, and reduces data centers’ carbon footprint with QCT QoolRack smart management.
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