Tech Giants Pledge to Train Millions of Workers in AI

February 7, 2024

Getting workers up to speed on the latest artificial intelligence technology has become a priority for many of the world’s top companies. That includes the te Read more…

University of Michigan’s ‘Zeus’ Framework Downsizes AI’s Massive Carbon Footprint

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…

Tesla Bulks Up Its GPU-Powered AI Super – Is Dojo Next?

August 16, 2022

Tesla has revealed that its biggest in-house AI supercomputer – which we wrote about last year – now has a total of 7,360 A100 GPUs, a nearly 28 percent uplift from its previous total of 5,760 GPUs. That’s enough GPU oomph for a top seven spot on the Top500, although the tech company best known for its electric vehicles has not publicly benchmarked the system. If it had, it would... Read more…

Cerebras Systems Thinks Forward on AI Chips as it Claims Performance Win

June 22, 2022

Cerebras Systems makes the largest chip in the world, but is already thinking about its upcoming AI chips as learning models continue to grow at breakneck speed. The company’s latest Wafer Scale Engine chip is indeed the size of a wafer, and is made using TSMC’s 7nm process. The next chip will pack in more cores to handle the fast-growing compute needs of AI, said Andrew Feldman, CEO of Cerebras Systems. Read more…

Microsoft’s ‘Singularity’ to Enable Global Accelerator Network for AI Training

February 24, 2022

In science fiction and future studies, the word “singularity” is invoked in reference to a rapidly snowballing artificial intelligence that, repeatedly iterating on itself, eclipses all human knowledge and ability. It is this word that Microsoft—perhaps ambitiously—has invoked for its new AI project, a “globally distributed scheduling service for highly efficient and reliable execution of deep learning training and inference workloads.” Read more…

Cerebras CS-2 Aids in Fight Against SARS-CoV-2

December 21, 2021

Decoding the replication mechanisms of the SARS-CoV-2 virus has been a key research quest as the COVID-19 pandemic continues. For the scientific computin Read more…

Nvidia Dominates Latest MLPerf Results but Competitors Start Speaking Up

December 1, 2021

MLCommons today released its fifth round of MLPerf training benchmark results with Nvidia GPUs again dominating. That said, a few other AI accelerator companies Read more…

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MosaicML, Led by Naveen Rao, Comes Out of Stealth Aiming to Ease Model Training

October 15, 2021

With more and more enterprises turning to AI for a myriad of tasks, companies quickly find out that training AI models is expensive, difficult and time-consuming. Finding a new approach to deal with those cascading challenges is the aim of a new startup, MosaicML, that just came out of stealth... Read more…

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Whitepaper

From Hallucination to Reality

As Federal agencies navigate an increasingly complex and data-driven world, learning how to get the most out of high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML) technologies is imperative to their mission. These technologies can significantly improve efficiency and effectiveness and drive innovation to serve citizens' needs better. Implementing HPC and AI solutions in government can bring challenges and pain points like fragmented datasets, computational hurdles when training ML models, and ethical implications of AI-driven decision-making. Still, CTG Federal, Dell Technologies, and NVIDIA unite to unlock new possibilities and seamlessly integrate HPC capabilities into existing enterprise architectures. This integration empowers organizations to glean actionable insights, improve decision-making, and gain a competitive edge across various domains, from supply chain optimization to financial modeling and beyond.

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Why IT Must Have an Influential Role in Strategic Decisions About Sustainability

Data centers are experiencing increasing power consumption, space constraints and cooling demands due to the unprecedented computing power required by today’s chips and servers. HVAC cooling systems consume approximately 40% of a data center’s electricity. These systems traditionally use air conditioning, air handling and fans to cool the data center facility and IT equipment, ultimately resulting in high energy consumption and high carbon emissions. Data centers are moving to direct liquid cooled (DLC) systems to improve cooling efficiency thus lowering their PUE, operating expenses (OPEX) and carbon footprint.

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