Running large language models locally sounds straightforward until you actually try it. Memory constraints, slow token generation, and retrieval quality all fight you at once. This is a walkthrough of what I learned building a RAG pipeline around local Llama inference with quantized models, including the tradeoffs that actually mattered in practice.
Why Local?
Cloud APIs are convenient but they come with limits: latency spikes, data privacy concerns, vendor lock-in, and cost that compounds quickly when you are iterating on prompts and retrieval strategies. For developer documentation assistance, the input often includes proprietary code and internal architecture details that should not leave the machine. Running everything locally eliminates those concerns entirely.
The tradeoff is that you are now responsible for the entire stack: model loading, quantization, inference serving, embedding generation, retrieval, and the chat interface that ties it all together. Each layer has its own set of knobs.
Quantization Choices
The first decision is how much precision you are willing to sacrifice for memory and speed. I tested three configurations: GPTQ 4-bit, LLM.int8(), and full fp16. GPTQ with 4-bit quantization brought a 7B model down to under 5 GB of VRAM while keeping generation quality serviceable for structured documentation answers. LLM.int8() was more conservative, preserving more nuance in longer responses, but required roughly 8 GB. Full fp16 was only viable on machines with 16 GB or more of VRAM.
In practice, GPTQ 4-bit gave the best balance. The quality drop was noticeable on creative generation tasks but barely relevant for factual retrieval where the model is grounding its answers in retrieved context. Response latency improved by roughly 40 percent compared to fp16.
Embedding and Retrieval
The retrieval side of the pipeline matters more than the model size for answer quality. I used sentence-transformer embeddings for document chunking and vector search. Chunk size turned out to be the single most important parameter. Too small (under 200 tokens) and you lose context. Too large (over 600) and you dilute relevance. I settled on 400-token chunks with 50-token overlap between consecutive chunks.
For the document ingestion pipeline, I built modular crawlers that could parse Markdown files, HTML documentation, and plain text. Each source went through the same processing path: strip boilerplate, chunk, embed, store in a local FAISS index. The entire index for a mid-size documentation site (roughly 200 pages) builds in under a minute on a single GPU.
Retrieval used a two-pass approach: an initial FAISS similarity search that pulls the top 10 chunks, followed by a relevance scoring pass that reranks and selects the top 4. This second pass made a measurable difference in answer quality, especially for queries where the most relevant information was split across different sections of the documentation.
Inference and Streaming
Serving the model through an OpenAI-compatible API endpoint (using llama.cpp's server) made it easy to swap the inference backend without touching the rest of the pipeline. Prompt templates were version-controlled alongside the code, which eliminated a whole class of debugging where the retrieval looked correct but the model response was garbage because of a wrong system prompt.
Streaming was essential for UX. Waiting 10 seconds for a full response feels broken. Streaming tokens as they generate gives immediate feedback that the system is working, even if total generation time is the same. The Next.js frontend consumed the streaming endpoint with a simple event source handler.
Containerization
Packaging the whole stack (model, embeddings, FAISS index, inference server, and frontend) into Docker containers made deployment reproducible. The main challenge was GPU passthrough and memory allocation inside the container. I used NVIDIA's container toolkit and set explicit memory limits to prevent the model from consuming all available RAM when multiple users hit the system simultaneously.
The final setup runs on a single machine with a consumer GPU. Total cold start to working chat interface is under 90 seconds. For a local-first developer tool, that is a reasonable tradeoff for keeping all data on the machine.
What I Would Change
If I were rebuilding this today, I would start with a more aggressive chunking strategy that preserves document structure (headers, code blocks, tables) rather than treating everything as flat text. I would also add hybrid retrieval earlier, combining dense embeddings with sparse BM25 scoring, because there are query types where keyword matching still outperforms semantic search. And I would move the reranking pass into a smaller cross-encoder model rather than relying on the generation model to do implicit reranking through longer context windows.