#local ai

Tonight I switched from Claude Code to OpenCode, so my development work utilizes and depends on computer hardware running in my local homelab. Meaning I am self-sufficient with how I use artificial intelligence; an exotic technological dream come true.

The immediate benefits of moving to OpenCode include:

1 - Significant cost reduction 2 - Zero telemetry or vendor/platform lock-in 3 - Deep hands-on learning and skills growth 4 - Complete control, responsibility and ownership

Setting up OpenCode was an uphill process of debugging and experimentation. But after several hours, I am using Gemma 4 12B (served via Ollama) inside Visual Studio Code. Which is a clear win in my opinion.

The limitations of using OpenCode versus a cloud AI provider are largely determined by the computers I have access to. An example of why I began investing in homelab equipment in the first place.

A mini PC the size of a paperback can now run a 200-billion-parameter model locally. But choosing one isn't about the lowest price tag. Capacity sets what fits, bandwidth sets how fast it runs, and the software stack — CUDA, ROCm, or Metal — decides whether your tools work at all. Here's how the four real options compare in 2026, with current prices and benchmarks. https://www.buysellram.com/blog/how-to-choose-the-best-mini-pc-for-local-ai-in-2026/

How to Choose the Best Mini PC for Local AI in 2026