Open source vs proprietary: control, dependency, and the real trade-off
Llama vs GPT, Mistral vs Claude, open weights vs API: the distinction isn't ideological, it's strategic. Understanding what you actually control in each case.
Sovereignty
On-premise, open source, hardware (GPU/APU), running AI yourself, US vendor dependency. Taking control without kidding yourself.
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Running AI on your own infrastructure: on-premise, OSS, GPU and APU
You don't need a datacenter to run a local LLM. A server with a consumer GPU or even a Mac M-series can host models from 7 to 70 billion parameters. What's possible, what isn't, and why it matters.