Managing a small local AI budget (Mac M2 16gb) (millfolio.com)

A recent post detailed the implementation of a local AI model for managing personal finance data on a Mac M2, focusing on optimizing the model's efficiency when handling thousands of transaction records. The challenge identified was the model's inability to scale efficiently for batch queries—like determining annual subscription expenses—due to the heavy computational load. To address this, the design emphasized pre-computing tags to store decisions, rather than performing real-time model inference during user queries. The tagging system uses three types: string tags for standard category matching, reference tags for hierarchical organization, and AI tags for complex classifications requiring model inference. By classifying transactions in batches, the system reduces redundancy and speeds up processing, allowing for quicker responses while preserving user device performance. The approach not only enhances efficiency but also improves privacy, as the local model operates without exposing sensitive financial data externally. This hybrid system leverages deterministic rules for reliability and AI for flexibility, showcasing a practical solution to local AI challenges in personal finance management.