LLM Moats Quickly Evaporating

Bryan Cockfield writes for Hackaday that the competitive edge once enjoyed by big AI firms is disappearing as open‑source large language models run on modest hardware. TerminalBytes demonstrated the twenty‑seven billion parameter Qwen‑3.8 on a Mac Studio equipped with two hundred fifty‑six gigabytes of RAM, and even a one‑bit quantized version handling a sixteen‑gigabyte machine. The article points out that the remaining barrier is simply having enough compute power, not proprietary data or training costs. If hardware remains the last moat, average gaming PCs may soon run these models locally.

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