I did the math on renting a model vs running one at home. The break-even was embarrassing.

说明与用途

在现有硬件上运行本地 LLM 通过约 7 个月实现收支平衡，消除了持续订阅费用并提升了隐私，日常任务的性能足够接受。

问题

Deciding whether to pay for a cloud-based LLM subscription or run a local model on personal hardware.

结果

Running a local LLM on existing hardware broke even after ~7 months, eliminating ongoing subscription costs and improving privacy, with acceptable performance for day‑to‑day tasks.

工作流

• 1. Identify cloud subscription cost ($20/seat/month).
• 2. Count active users (4) to calculate monthly and yearly spend.
• 3. Assess local hardware: old workstation, used GPU, free LM Studio/Open WebUI, quantized models.
• 4. Measure power consumption (≈ small space heater).
• 5. Compare cumulative costs over time to find break-even point (~7 months).
• 6. Evaluate model performance trade-offs (smaller models, limited context).
• 7. Deploy local setup using Docker for easy backup and cleanup.
• 8. Use local model for drafting, summarizing, and internal tasks.
• 9. Optionally purchase a self-hosted AI home lab bundle for setup guidance.

证据

The break-even landed somewhere around the seven-month mark.

来源

The only real ongoing cost was power, and honestly a mid‑range GPU idling at a spooled‑down quant draws less than people assume.

来源