November 29, 2023
Editors Note: Additional Coverage of the AWS-Nvidia 65 Exaflop ‘Ultra-Cluster’ and Graviton4 can be found on our sister site Datanami. Amazon Web Service Read more…
October 16, 2023
If you are waiting in a giant line for Nvidia's H100 GPUs, be advised that the next-generation H200 chip is already on its way. The GPU maker earlier this mo Read more…
August 17, 2023
The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…
July 25, 2023
Nvidia is now renting out its homegrown AI supercomputers with its newest GPUs in the cloud for those keen to access its hardware and software packages. Th Read more…
May 29, 2023
At the Computex event in Taipei this week, Nvidia announced four new systems equipped with its Grace- and Hopper-generation hardware, including two in Taiwan. T Read more…
May 28, 2023
We in HPC sometimes roll our eyes at the term “AI supercomputer,” but a new system from Nvidia might live up to the moniker: the DGX GH200 AI supercomputer. Read more…
October 17, 2022
Oregon State University (OSU) is planning to launch an expansive, expensive – $200 million – new research and education center. The center will be named aft Read more…
March 22, 2022
At GTC22 today, Nvidia unveiled its new H100 GPU, the first of its new ‘Hopper’ architecture, along with a slew of accompanying configurations, systems and 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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