A primer on key AI LLM concepts — KV cache, weights, quantization, and how inference actually works
TL;DR
How do AI models work? This primer explains crucial concepts to understand how LLMs work, with definitions and explanations for all of AI's new nomenclature. How do AI models work? This primer explains crucial concepts to understand how LLMs work, with definitions and explanations for all of AI's new nomenclature.
Nauti's Take
For a small team, this works best as an infrastructure checklist: where do your model costs come from, and how do context length, KV cache, and quantization behave in your deployment? Run a small load test with your real prompt lengths before drawing conclusions about latency, memory use, or output quality from general explanations.
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