Hardware decides what you can actually run, and the specifications that matter are not always the ones in the marketing material.
Threads cover GPU selection for training versus inference, VRAM as the binding constraint it usually is, consumer versus datacentre parts, Apple Silicon unified memory and where it wins, inference optimisation and serving throughput, and cloud instance selection against the cost of owning hardware.
Local build writeups are a staple here and among the most useful content on the site. Actual part lists, actual costs, measured tokens per second and the thermal and power reality of running a rig continuously are all things specification sheets do not give you.
The build versus rent question comes up constantly and has no single answer. Utilisation rate decides it, and members who have done the arithmetic both ways are worth reading.
See Open-Source & Efficient Models for what to run on it and Enterprise AI Platforms for managed alternatives.
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