Nvidia's large-language models will become generally available later this year, the company confirmed.
Organizations widely rely on Nvidia's graphics processors to write AI applications. The c …
MLCommons this week issued the results of its latest MLPerf Inference (v3.1) benchmark exercise. Nvidia was again the top performing accelerator, but Intel (Xeon CPU) and Habana (Gaudi1 and 2) pe …
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September 10, 2023
The shortage of Nvidia's GPUs has customers searching for scrap heap to kickstart makeshift AI projects, and Intel is benefitting from it. Customers seeking qui Read more…
September 7, 2023
Zapata Computing, the quantum software company spun out from Harvard in 2017, yesterday announced plans to go public and reposition itself as a provider of indu Read more…
August 31, 2023
Google scientists Jeff Dean and Amin Vahdat delivered a fascinating tour of major ML hardware and software design trends in their joint Hot Chips 23 opening k Read more…
August 24, 2023
The launch of ChatGPT and similar generative AI technologies is reshaping the skills required in the workplace, according to a new report from LinkedIn. “The Read more…
August 3, 2023
The success of GPU coprocessors has invited developers to focus on combined CPU/GPU performance and optimization. Primarily by using CUDA, (and OpenCL, OpenACC, Read more…
July 24, 2023
How will quantum computing be put to use in high energy physics? That was the subject of an international workshop last November. Now the group - the Quantum Co Read more…
July 19, 2023
It is becoming clearer that China's plan to cut reliance on Western chip technology revolves around homegrown chips built using the open RISC-V architecture, wh Read more…
July 13, 2023
Remember when a GPU was a small fan-less video card with names like Voodoo, Matrox, Nvidia, or ATI? This simple addition gave your PC a new world of responsive Read more…
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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