In the past, LLMs used to be large and complex, and their operational infrastructure was inevitably large, but in recent years, smaller, more efficient LLMs with far fewer parameters are on the rise.
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 get 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 addition to the lack of mobility in operating large AI workloads in the past.

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.
Learn how AI workload execution at the edge has become a reality and the challenges faced by large-scale AI execution environments.

It also explains how these challenges can be solved by edge AI and how CPU solutions can contribute.