In Part 1 of this series, I set up Claude Code against local LLMs on dual MI60 GPUs and watched it scaffold a Flask application from scratch. Small tasks worked. Complex ones did not. I ended with three ideas I wanted to test: running a dense model, trying Claude Code’s agent teams feature, and building a persistent memory layer for coding sessions.

I started the experiment that mattered most: giving a local LLM a project with real scope and seeing what happened. I ran the same project against two different models. The results were instructive, and not in the direction I expected.

Two AMD MI60s from eBay cost me about $1,000 total and gave me 64GB of VRAM. That’s enough to run Llama 3.3 70B at home with a 32K context window.

When I started looking into running large language models locally, the obvious limiting factor was VRAM. Consumer GPUs top out at 24GB, and that’s on an RTX 4090 at the high end. I wanted to run 70B parameter models locally, on hardware I own.