Local RAG systems re-read documents, causing delays, user finds

A user discovered that their local Retrieval-Augmented Generation (RAG) system, powered by llama.cpp on an Apple M4 Pro, was not inherently slow but was inefficiently re-reading entire documents for every query. This led to significant delays, with prefill (reading the context) dominating the processing time rather than the actual answer generation. The user identified two key issues: duplicated tokens from multiple retrievers and a non-functional prefix cache, which, when partially addressed, reduced query times but highlighted a fundamental inefficiency in how RAG systems process context. AI

IMPACT Highlights a critical inefficiency in RAG systems that may slow adoption and requires developer attention.

RANK_REASON User-authored technical analysis of a common LLM system inefficiency.

Read on dev.to — LLM tag →

• Apple M4 Pro
• boron-14
• llama.cpp
• retrieval-augmented generation

• infra
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →