People often speculate about their personal finances: "Has my spending on groceries increased compared to past year?", "How does my spending on clothes look for the past 6 months?". However, answering them with cloud-hosted LLMs transmits sensitive financial data to external servers, exposing users to data leakage and inferential privacy risks. We present SpendSight, a study of fully on-device personal finance Q&A tool calling that keeps all data strictly local. We evaluate seven quantized models (six sub-2B and one 3B) across 115 test cases and five analytical tools, comparing single-agent against dual-agent (router-specialist) architectures. Dual-agent decomposition yields the largest time gains for the Qwen3.5 family. Qwen3.5-0.8B achieves 61.7% Correct Ratio (89.1% Task Accuracy) in 3.0 s with 1.2 GB RAM in dual-agent mode. A 3B reference model (G9v3-3B, Q4_K_M) achieves 71.7% Correct Ratio (90.6% Task Accuracy) at 2.3 s in single agent mode, suggesting that larger models with aggressive quantization can bypass decomposition.