AI inferencing refers to the process of using a pre-trained model, such as GPT-4 or Claude 3, to analyze new data and generate inferences or probable outcomes. This technique is commonly applied in areas like chatbots, coding assistance, and image recognition. However, traditional models may struggle with specific queries that require proprietary data not included in their training.

Retrieval-Augmented Generation (RAG) enhances AI inferencing by integrating external data sources that the original model was not trained on. This connection allows the model to access domain-specific data, resulting in more accurate and relevant outputs. For instance, an insurance model can provide better insights by incorporating customer-specific data alongside general population data.

Cisco AI PODs for Inferencing are CVD-based solutions designed for Edge Inference, RAG, and Large-Scale Inferencing. They facilitate accelerated deployment with centralized management and automation. These solutions have been performance tested to demonstrate linear scalability, ensuring consistent performance across varying dataset sizes. Each configuration includes essential components like the Cisco UCS X-Series Modular System and Nvidia GPUs, making them suitable for both data center and edge AI deployments.