Optimizing a Local RAG Chatbot for CPU Inference

Tags: #ai #llm #rag #python

Building a local RAG chatbot is not just about choosing an LLM. It requires balancing response quality, retrieval accuracy, inference speed, and resource utilization.

Here are the key optimizations I explored and implemented:

1. Model Quantization

Used a small, 4-bit quantized Qwen Instruct model to reduce memory requirements and make CPU-based inference more practical.

2. Retrieval Optimization

Improved document chunk quality and evaluated query-to-context similarity to retrieve more relevant information and reduce unnecessary context.

3. Similarity Threshold Tuning

Fine-tuned retrieval thresholds to balance relevance and recall, filtering weak matches without unnecessarily discarding useful information.

4. Controlled Generation

Kept the LLM temperature around 0.2 to encourage consistent, focused, and natural responses. Factual grounding remained dependent on retrieval quality and prompt design.

5.…