Intel is making a serious play for the enterprise RAG market, and the pitch is straightforward: your retrieval-augmented generation stack doesn’t need to run on expensive, supply-constrained GPUs. The company’s latest reference architecture pairs Gaudi 2 AI accelerators with Granite Rapids Xeon CPUs, and the performance claims are worth paying attention to.
The setup is built on OPEA, Intel’s Open Platform for Enterprise AI, with LangChain handling the orchestration layer. The rag-redis template does the heavy lifting, using BAAI’s bge-base-en-v1.5 embedding model and Redis as the vector store. Embeddings run on the Granite Rapids CPU, which Intel says delivers a 2-3x performance boost for mixed AI workloads thanks to its AMX-FP16 instruction set support. That’s not marketing fluff—FP16 acceleration on CPU is a genuine architectural shift that changes the math on when you need a discrete accelerator for embedding workloads.
The LLM itself runs on Gaudi 2 through Hugging Face’s Text Generation Inference server, with the Optimum Habana library bridging Transformers and Gaudi hardware. Intel uses the NeuralChat 7B model as the default, pulling from the same open-source ecosystem developers already know. The sample application ingests Nike’s 10-K filings to build a financial document Q&A system—a realistic enterprise use case, not a toy demo.
What’s notable here is how much Intel has smoothed the deployment path. Pre-built Docker images eliminate the setup friction that historically plagued Habana hardware. Developers can launch a TGI container with a single command, pass a model name, and have a working LLM endpoint on port 8080. That’s the kind of developer experience that actually drives adoption.
The TCO argument is where this gets interesting for enterprises. Gaudi 2 has been available on Intel Developer Cloud for over a year now, but pairing it with Granite Rapids for the embedding layer creates a full-stack story that doesn’t require a single NVIDIA component. For companies running RAG at scale—where embedding costs often dwarf generation costs—shifting that workload to optimized CPUs could be the difference between a pilot and a production deployment. Whether developers actually bite depends on how well Optimum Habana keeps pace with the breakneck speed of open-source model releases, but the foundation is more solid than Intel’s previous AI software efforts. For teams already invested in the LangChain ecosystem, this is a low-friction path to test the hardware without committing to a full migration.
