I guess one can always does the transition, I'm just wondering how to bridge these two areas, if there are any transferrable skills, or if someone ever sees an low-level perf/linux kernel engineer transitions to an LLM inference/model perf engineer?

A lot of CUDA tutorials throw the whole programming model at you almost immediately.

You learn the syntax, then grids and blocks, then memory types, then synchronization, and pretty quickly you’re juggling a bunch of concepts before you have a good feel for what CUDA is enabling you to do.

I wanted to try a slower approach.

Part 1 is mostly about building the basic mental model: what a kernel is, what it means to launch lots of threads, how those threads identify the piece of work they own, and how to start thinking about a problem in GPU terms.

The hardware details come later.

I wrote this mainly for programmers who are curious about CUDA but haven’t spent much time with GPU programming yet.

Would be interested in feedback from people here, especially on whether the progression feels natural or whether I’m introducing something in the wrong order.

Part 1: https://computejunkie.substack.com/p/cuda-programming-for-the-curious
Part 2: https://computejunkie.substack.com/p/cuda-programming-for-the-curious-f8f