L88 – A Local RAG System on 8GB VRAM (Need Architecture Feedback)
Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation. I’d really appreciate feedback on:…
In plain words
L88 is a local retrieval-augmented generation system designed to run on modest hardware, using 8GB of GPU VRAM with CPU-based embeddings and preprocessing. It is for developers interested in building RAG systems on resource-constrained machines. The project emphasizes UI/UX but acknowledges that its retrieval and model architecture require refinement, including resolving the limitation where the same model handles both evaluation and generation tasks.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation. I’d really appreciate feedback on: Better architecture ideas for small-VRAM RAG Splitting evaluator/generator roles effectively Improving the LangGraph pipeline Any bugs or design smells you notice Ways to optimize the system for local hardware I’m 18 and still learning a lot about proper LLM architecture, so any technical critique or suggestions would help me grow as a developer. If you check out the repo or leave feedback, it would mean a lot — I’m trying to build a solid foundation and reputation through real projects. Thanks!
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