Local RAG for NZ Tenancy Law - Qwen3-8B on RTX 4060
In this detailed article, we explore the implementation of a locally-trained retrieval-augmented generation (RAG) system tailored for New Zealand residential tenancy law. The project leverages cutting-edge AI architectures, including the Qwen3-8B-Q5_K model, optimized for high-performance inference on a single RTX 4060 GPU.
The core objective was to create a seamless integration of up-to-date legal documents from over 31,000 public Residential Tenancies Tribunal decisions, alongside relevant sections from the Residential Tenancies Act. By fine-tuning the model and deploying it on a dedicated hardware setup, the system achieves real-time, context-aware legal reasoning.
The technical design emphasizes performance and precision, with a focus on retrieval from large-scale legal corpora. Key components include:
System Architecture
The architecture comprises a fine-tuned Qwen3-8B-Q5_K instance, paired with a PostgreSQL-based knowledge retrieval engine. This setup ensures efficient access to structured legal documentation while maintaining low latency during inference.
Hardware and Optimization
The deployment utilized an RTX 4060 GPU, enabling GPU-accelerated processing. Key optimizations involved model quantization and efficient query indexing techniques to support live legislation extraction.
The lessons learned highlight the importance of aligning AI model capacity with the complexity of legal content. While the system delivers robust performance, further enhancements in explainability and user feedback loops are recommended.
Note: This article provides a technical overview and is not intended as legal advice. For specific inquiries, users are encouraged to refer to official legal resources.