Best Local Model for RAG Is the Wrong Question, Developer Argues
Cautious_Bit_8521 · reddit · 2026-08-08
The author argues that choosing a model based on leaderboards is the wrong approach for local RAG systems. A model ranking first in isolation doesn't guarantee it's the right choice once retrieval, quantization, hardware constraints, and latency are factored in.
The proposed evaluation method fixes Milvus as the retrieval tool, freezing the corpus, chunking, and test queries. Models are then tested with both retrieved and known-good evidence to separate retrieval misses from generation failures. Quantization should be treated as a core part of model configuration, not a footnote.
The conclusion is that there is no single "best model" for local RAG, only a useful frontier of tradeoffs involving grounded-answer quality, memory, latency, privacy, and operating cost.
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