#4436 Nvidia DGX Spark Fine-Tuning vs RAG; +NPR 7150.2D Knowledge Graph
#4436 Nvidia DGX Spark Fine-Tuning vs RAG; +NPR 7150.2D Knowledge Graph #4436 Given the capabilities of the Nvidia DGX Spark, would I get better results using RAG for highly technical documents, or by fine-tuning a small model with the technical document data? When dealing with highly technical documentation, choosing between Retrieval-Augmented Generation (RAG) and Fine-Tuning requires looking closely at how your hardware handles memory and data processing. The Nvidia DGX Spark , powered by the GB10 Grace Blackwell Superchip , features 128GB of LPDDR5x unified memory (UMA) with 273 GB/s bandwidth. Because the CPU and GPU share this single memory pool dynamically, you can comfortably run and manipulate local models up to 120B–200B parameters at compressed precisions (like FP4 via TensorRT-LLM or llama.cpp) without hitting traditional VRAM wall bottlenecks. Given these precise hardware capabilities and the nature of highly technical data, RAG (specifically GraphRAG or hybrid RA...