I mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)

I kept answering the same question for friends ("I've got a 16GB MacBook / a 3060, what can I actually run?") and got tired of guessing, so I started a spreadsheet. It grew into a real dataset, so I put it on GitHub under CC BY for anyone to use or fix. Rule of thumb I landed on: at Q4_K_M a model needs roughly 0.6GB of memory per billion params, and you want to size to about 70% of your RAM/VRAM so the OS, context and KV cache still have room.