F1 · Edge AI & Agents
CPUs in AI: A Practical RAG
Overview
On a DGX Spark, this project builds a local RAG pipeline, placing document cleaning, chunking, embedding, FAISS indexing and retrieval on the Grace Arm CPU while running language-model generation on the Blackwell GPU via llama.cpp. The architecture chains private-data processing, vector search and answer generation with an explicit CPU/GPU workload mapping. Its ecosystem value is concretely defining the CPU's data-processing and orchestration role in a heterogeneous AI system, and offering an understandable end-to-end RAG baseline.
Scope of verification
- The workflow uses a specific DGX Spark, embedding/generation models, FAISS and a sample corpus to validate the full path from document processing to retrieved-context generation; it does not provide a general CPU/GPU performance comparison.
- This workload mapping does not necessarily apply to all RAG systems; retrieval-quality evaluation, data updates, access control, observability, concurrency, high availability and production serving are not covered.
Canonical source: https://developer.arm.com/community/arm-community-blogs/b/ai-blog/posts/rethinking-the-role-of-cpus-in-ai-a-practical-rag-implementation-on-dgx-spark
This page is a guided summary with boundary notes, not an official reproduction.
This page is a guided summary with boundary notes, not an official reproduction.