Originally published on AI Tech Connect.
What you need to know Running a language model on the hardware you already own — a laptop, a single consumer GPU, an Apple Silicon Mac, or a small edge box on a factory floor — is no longer a research curiosity. It is a deployment pattern that ships. The two decisions that make or break it are which model you choose and how aggressively you quantise it. Get them right and a 7-to-8-billion-parameter model runs happily on an 8GB GPU or a 16GB Mac, with no API bill and no data leaving the device. Get them wrong and you either run out of memory before the first token or watch quality fall off a cliff on the one task you actually care about. GGUF Q4_K_M is the default — it runs on CPU, consumer GPUs and Apple Silicon, keeps roughly 92% of full-precision quality and cuts model size around 75%…
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