Every machine can run a local LLM (here's what fits)
Per-tier guide: 8GB integrated graphics, 16GB MacBook Air, 8/12/16/24/32GB VRAM PCs, 24/32/64/128/192GB Macs. Specific models, specific tok/s, specific configs. Every tier runs something useful.
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#hardware
Posts on AI engineering, LLM systems, and software development.
Why Apple Silicon punches above its weight on local LLMs
Unified memory means the GPU sees all of RAM. Why that beats discrete-GPU PCs above 32B parameters, what fits in 16/32/64/128/192GB, and where Apple Silicon still loses.
What it takes to run a model on your machine
Why VRAM is the hard ceiling on local LLMs, what quantization actually does to a model file, and the practical hardware ladder from 8GB laptops to 192GB workstations.
Where AI actually runs: cloud, local, edge
Where the model file actually sits when you use AI: a datacenter GPU (cloud), your own machine (local), or the device's silicon (edge). The trade-offs and how to pick.