Cost-efficient Enterprise RAG with Intel Gaudi 2 and Xeon

AI Impact Summary

This article outlines a cost-optimized RAG stack using Intel Gaudi 2 accelerators for LLM inference and Xeon CPUs for embeddings, with LangChain and Redis as the vector store. It emphasizes separating data from learned parameters to balance performance, security, and privacy, and highlights hardware-accelerated throughput gains (AMX-FP16, FP8 quantization) and lower TCO for data-center or IDC deployments. The approach leverages the Open Platform for Enterprise AI (OPEA), with optimizations to LangChain and a concrete RAG pipeline (rag-redis template, BAAI/bge-base-en-v1.5 embeddings, Redis) to deliver cost-efficient enterprise-grade AI. Practically, organizations should plan for containerized deployment (TGI Gaudi container), ensure embedding on Xeon, LLM on Gaudi 2, and be prepared to scale with multiple Gaudi accelerators if using larger models like 70B variants.