DELLQUINN: A Machine Learning-Based, Resource Capacity Optimization Solution

whitePaper | October 18, 2022

Tachyum Prodigy is the industry’s first universal processor, unifying the functionality of CPU, GPU, and TPU into a single monolithic package. Prodigy’s revolutionary new architecture provides 6x more raw performance on AI training and inference workloads than the industry’s highest performing GPU, and up to 10x performance at the same power.

whitePaper | August 1, 2020

One of the first management funds in China, Fullgoal Fund overcame the need for massive data storage and support for large numbers of virtualized servers with a Nutanix hyperconverged infrastructure and enterprise cloud solution.

whitePaper | September 29, 2021

Penguin Computing’s Analytics Practice acts as a single point of reference for hardware, software, architectural design, and much more, enabling organizations to focus their energy on their AI projects. Access this white paper to learn more about this secure, end-to-end solution designed to alleviate AI infrastructure woes.

whitePaper | June 23, 2020

Modern organizations face unprecedented threats to their critical information assets and data. In previous years, the mindset of many cybersecurity professionals and business leaders was focused on avoiding attacks and building a strong perimeter to deflect them. However, more recently this approach has shifted, and these leaders now understand that attacks are inevitable. With this fundamental shift in thinking, cybersecurity professionals must build strong incident response programs that are capable of detecting threats in a timely manner and responding effectively when they occur.

whitePaper | September 19, 2022

The field of artificial intelligence (AI) traces back to the 1950s. More recently, the increased availability of both data and computing power has bolstered rapid adoption of AI and machine learning (ML) solutions (collectively, AI/ML)

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.