May 23, 2026
GPU gossip goes brrrr
Making Deep Learning Go Brrrr from First Principles
Why your AI runs slow — and why the comments got way more heated than the math
TLDR: The article says speeding up AI starts with a simple question: is your machine doing useful math, moving data around, or wasting time elsewhere? Readers loved the mind-bending scale, argued over whether the rules are too neat, and turned one weird code example into the thread’s main character.
A deep learning engineer tried to do the impossible: turn the black magic of speeding up AI into something almost normal-person understandable. The big message of the post is simple: stop throwing random tricks at your code and first figure out what’s actually slowing things down. Is the machine spending time doing the real work, shuffling data around, or just waiting on software overhead? In other words, before you buy a faster sports car, maybe check whether you’re stuck in traffic.
But the real fun was in the comment section, where readers immediately split into three camps: the mind-blown, the aspirational, and the deeply suspicious. One commenter dropped the jaw-on-floor line that in the time Python does one floating point operation — a tiny math step — an Nvidia A100 chip can blast through 9.75 million of them. That got the classic internet response: “wild.” Another reader showed up with the academic side-eye, linking a paper to push back on the article’s neat advice about overfitting, a reminder that in AI, even the “basic rules” can start fights.
Then came the relatable energy. One person admitted they currently just download ready-made models from Hugging Face but dream of building a tiny chatbot from scratch someday — the tech equivalent of saying, “I microwave dinner now, but one day I’m opening a restaurant.” And the funniest mini-drama? A baffled commenter asking how x.cos().cos() could possibly be faster than calling cosine twice separately. Translation: readers were not about to let “just trust the optimizer” slide without a courtroom cross-examination. So yes, the article was about making GPUs go brrrr — but the comments were the real high-speed event.
Key Points
- •The article frames deep learning system performance in three components: compute, memory bandwidth, and overhead.
- •It argues that identifying whether a workload is compute-bound, memory-bandwidth bound, or dominated by overhead helps eliminate ineffective optimization strategies.
- •The article gives examples of mismatched optimizations, such as faster GPUs not helping memory-bound workloads and lower-overhead code not helping compute-bound workloads.
- •It states that maximizing time in the compute-bound regime is desirable because computation usually cannot be reduced without changing the underlying operations.
- •The article notes that compute capacity has grown faster than memory bandwidth, making full hardware utilization increasingly difficult.