75% of leaders see the use of generative AI and machine learning as providing a competitive advantage

In the past, the LLMs that funded generative AI were usually large and complex, and their operational infrastructure was inevitably large.
As AI-related technologies have matured, the trend has shifted from traditional centralized AI execution environments to running AI closer to the edge or at the edge itself.

The ability to run AI on PCs, smart devices, and the IoT is expected to dramatically increase energy and cost efficiency and minimize latency to obtain computational results.
In addition, many have voiced concerns about the security of the servers that serve as the operational execution infrastructure for AI and LLM, as well as the increased power consumption of data centers.

In response, we are focusing on a solution that combines a computing platform capable of handling cutting-edge AI functions and workloads with dedicated CPUs.
The solution is based on open source, which means that it can be instantly adapted to new AI models through the power of the community, and it is highly customizable.

It identifies the current reality of running AI workloads at the edge and the challenges faced by large-scale AI execution environments.
It also explains how those challenges can be solved by edge AI and how CPU solutions can contribute to the solution.