As the AI sector evolves, organizations must decide the optimal environment for their AI processes, weighing cost against energy usage and performance. Companies like DeepSeek and 01.AI have trained impressive models on minimal budgets, but the infrastructure costs for large cloud-based AI are significant.
An edge computing model provides key advantages, such as automatic scaling and energy efficiency through query caching. By optimizing query handling at the edge, organizations can achieve substantial energy savings, with many estimating reductions between 10% and 50%. However, the implementation of effective caching techniques remains a challenge for many, often requiring specialized skills to execute properly. Therefore, while hybrid models balancing edge and cloud might serve best, making caching solutions accessible is crucial for broader adoption.
👉 Pročitaj original: CIO Magazine
