December 6, 2023
“What’s the size of the AI market?” It’s a totally normal question for anyone to ask me. After all, I’m an analyst, and my company, Intersect360 Res Read more…
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…
November 28, 2023
Quantum computing held sway in the last few minutes of AWS senior vice president Peter DeSantis’ keynote yesterday at the AWS re:Invent 2023 conference, being Read more…
October 16, 2023
Preventing signal loss and garbling are key goals in developing effective quantum networks and eventually a quantum Internet. Today, AWS and Harvard researchers Read more…
July 27, 2023
The AI supercomputing options in the cloud have expanded at an unprecedented rate over the last few weeks. Amazon joined the party on Wednesday by announci Read more…
July 11, 2023
Quantum networking – like quantum computing – holds tantalizing promise. Rather than use classical bits, quantum networks work with quantum bits (qubits) an Read more…
June 22, 2023
AWS has finally made available its Arm-based CPUs available for supercomputing – but it's not a chip you can buy off the shelf. The chip, Graviton3E, is acces Read more…
May 2, 2023
A fascinating ACM paper by researchers Torsten Hoefler (ETH Zurich), Thomas Häner (Microsoft*) and Matthias Troyer (Microsoft) – "Disentangling Hype from Pra 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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