👀 If you're wondering why Mac minis have sold out to people running OpenClaw the secret isn’t branding. It’s unified memory.

With Apple Silicon, the CPU and GPU share one large memory pool. No VRAM ceiling. No constant data shuffling over PCIe. For large language models, that’s a huge deal. These systems are memory-hungry and love big, contiguous blocks of RAM.

A Mac Studio with 64–128GB unified memory can run models that would choke on many consumer GPUs. Once you hit 128GB, 70B-parameter class models become genuinely playable with quantisation. That’s serious local experimentation territory.

Does a NVIDIA RTX 4090 still win on raw tensor throughput? Absolutely. If your model fits in 24GB VRAM, it’ll fly.

But for solo builders, founders, and engineers who want to run big models locally without building a datacenter in their spare room, unified memory quietly changed the game.

The bigger shift isn’t Mac vs PC.

It’s that “AI lab hardware” now fits on a desk.

And that lowers the barrier to serious experimentation more than most people realise.

#BuildInPublic #Startups #PromptEngineering

Originally posted on LinkedIn.

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