Blogs

Large Language Models (LLMs) are revolutionizing our interactions with technology. However, their size typically makes them resource-intensive, driving up costs and energy consumption.

Drastic efficiency improvements are waiting for you. Kick x86 to the curb and bring in AmpereOne. By repurposing freed-up rackspace, you can invest in the future and modernize your infrastructure.

The Chimera, an iconic hybrid in Greek mythology, combines a lion, a goat, and a serpent. It features a lion's head, a goat's body with another goat's head on its back, and a serpent's tail, and it breathes fire.

On Feb 26, 2024, the White house office of the National Cyber Director released a report asking the technical community to proactively reduce the attack surface in cyberspace.

Have you noticed that despite loading our personal devices with multiple applications, we can almost always watch our favorite soccer or cricket game without interruption?

For years, AI enthusiasts believed that powerful and costly GPUs are essential for training and running complex large language models (LLMs) like ChatGPT and Llama 2. But the landscape is changing

An innovation in CPU architecture—Cloud Native Processors—is providing a viable alternative to the high cost and power consumption of x86 for AI inferencing.

As more processing moves to the edge, edge compute is evolving to be cloud native in environments restrained by power limits and a need to generate less heat.

This week during their HPE Discover 2022 conference, HPE announced the launch of the HPE ProLiant RL300 Gen11 platform, powered by Ampere