Whether you are trying to squeeze more tokens per second out of a Large Language Model (LLM), shave milliseconds off inference, or just understand why your training loop runs slower than the spec sheet promises, the path eventually runs through profiling.

The catch is that profiling has a steep on-ramp. The traces are dense walls of colored rectangles. The events carry intimidating names. Most tutorials assume you can already read them. So even when we know we should be profiling, opening a trace can feel like a chore best left for later (or for someone else). This post, and the series it kicks off, is our attempt to lower that on-ramp.

This is the opening post of Profiling in PyTorch, a series where we slowly build the skill of reading profiler traces and use it to drive optimization. The plan:

We document the journey from a beginner's point of view. No prerequisites apart from basic PyTorch. Treat this as a leisurely read with some "Aha!" moments. The structure of the post is intentionally question-led: we open a trace, ask "wait, why is that happening?", and chase the answer until something clicks. By the end you should know:

You don't usually have to write GPU kernels yourself; when you use a PyTorch operation, it is automatically translated to one or more kernels that do the job on GPU.

Here is the entire script that we use for the post: 01_matmul_add.py. We recommend opening this script in a separate tab and walk through the code step by step. We use the NVIDIA A100-SXM4-80GB GPU to run the scripts.