Linear Digressions

GPUs are the engines powering every AI conversation you've ever had — but what's actually happening inside those chips when an LLM generates a response? Understanding GPU fundamentals turns out to be surprisingly useful for making sense of why inference works the way it does, from the prefill stage that processes your entire prompt at once to the serial decode step that produces tokens one by one. If you want to understand the real constraints shaping modern AI systems, this is where to start.

The unsung hero of every LLM conversation you've ever had? A clever optimization called the KV cache. Self-attention is computationally brutal — every token attending to every other token means costs that scale as N², and that blowup gets worse with every turn of a conversation. The KV cache is what keeps that from becoming completely intractable at scale. Today's episode goes deep on how it works, why inference is a harder and more interesting problem than it gets credit for, and what's really happening under the hood when you chat with an LLM.

Everyone talks about training the model — the data, the compute, the alignment magic. But what happens after the weights are frozen and millions of users start hammering it with prompts? That's inference, and it's a surprisingly deep rabbit hole of engineering challenges that rarely gets the spotlight it deserves. This week I kick off a new series unpacking the full inference ecosystem — from the moment a model completes training to the moment tokens land in your hands.

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