Technical Analysis of Local RAG Implementation: Tradeoffs Between Inference Speed and Retrieval Accuracy
By
tmaly
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Summary
The article discusses local RAG (Retrieval-Augmented Generation) implementation, focusing on model performance tradeoffs between inference speed and retrieval accuracy. The author shares insights from interactions with model creators, comparing different approaches including a 32M parameter model that uses tokenization + lookup table + averaging versus a 23M parameter transformer-based model. The discussion covers technical details about inference speeds (~22 documents per second on CPU) and the performance compromises involved in different architectural choices for local RAG systems.
Key quotes
· 4 pulledThe tradeoff here is that you get even faster inference, but lose on retrieval accuracy
Specifically, inference will be faster because essentially you are only doing tokenization + a lookup table + an average
So despite the fact that their largest model is 32M params, you can expect inference speeds to be higher than ours, which 23M params but it is transformer-based
I am not sure about typical inference speeds on a CPU for their models, but with ours you can expect to do ~22 docs per second
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