Efficient Local Intelligence
Exploring Small Models, Local Inference and Context Efficiency
Investigating how small language models, local inference, retrieval and intelligent context selection can be combined to deliver efficient AI systems on resource-constrained hardware.
Problem Statement
Large cloud-dependent AI models create cost, privacy and latency barriers for many use cases. This research investigates whether smaller, locally-deployed models can achieve practical intelligence with appropriate engineering.
Research Questions
- 01 What performance is achievable from quantised small models on consumer hardware?
- 02 How can retrieval and context engineering compensate for reduced model capacity?
- 03 What hybrid local/cloud routing strategies optimise cost and performance?
Results & Findings
This project has not yet entered active experimentation. Research questions and methodology are under development.
Publications
No publications available yet. Papers and technical reports will appear here when research is completed and passes independent review.