vukrosic

vukrosic / gpu-kernel-engineer-from-scratch

Public

Become GPU kernel engineer step by step.

42
15
100% credibility
Found May 12, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A structured 12-month self-paced course with weekly assignments to learn GPU programming for AI, culminating in a public portfolio of kernels, benchmarks, and explanations.

How It Works

1
🔍 Discover the Course

You find a free 12-month learning path on GitHub that teaches how to build fast computer operations for AI, step by step, with videos and guides.

2
🛠️ Set Up Your Workspace

You prepare your computer following simple instructions so it's ready for hands-on GPU lessons without any hassle.

3
📖 Start Week One

You read the first lesson on how GPUs think differently from regular computers and complete easy starter tasks to build your understanding.

4
📚 Follow Weekly Lessons

Each week, you learn one new skill like adding numbers super fast or handling big math operations, adding notes and results to your personal collection.

5
📊 Test and Measure Speed

You check if your creations work right and time how fast they run, watching your skills improve with real numbers and charts.

6
👥 Get Support if Needed

You can join a friendly community to ask questions, share progress, and stay motivated on your year-long journey.

🎉 Celebrate Your Portfolio

After a year of building, you have a public showcase of your work with explanations ready for job interviews in AI systems.

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Star Growth

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AI-Generated Review

What is gpu-kernel-engineer-from-scratch?

This Python-based repo delivers a 12-month roadmap to become a GPU kernel engineer from scratch, starting from basic GPU mental models and progressing to writing CUDA and Triton kernels for AI ops like softmax, matmul, layer norm, and attention. You follow weekly lessons to implement, test against baselines, benchmark performance, and build a public portfolio repo ready for ML systems interviews. It solves the gap for Python/PyTorch users who lack deep GPU systems knowledge and a tangible showcase of kernel skills.

Why is it gaining traction?

Unlike scattered tutorials, it compounds skills week-by-week into a polished portfolio, with built-in benchmarking, testing harnesses, and recovery plans to avoid burnout. Developers hook on the promise of shipping interview-ready artifacts—like fused kernels and perf reports—while pondering if GPUs will become cheaper, more expensive, or obsolete amid AI demands. The free structure plus community for accountability stands out for those aiming to become GitHub contributors or campus experts.

Who should use this?

Aspiring GPU engineers or ML engineers transitioning to AI infrastructure roles, especially if you know Python/PyTorch but want to own custom kernels for transformers or inference. It's ideal for self-taught devs building a GitHub star-worthy repo to land kernel engineering jobs, or students chasing GitHub developer program membership. Skip if you're already profiling production Triton code.

Verdict

Solid scaffold for motivated learners to become GPU kernel engineers, but at 42 stars and 1.0% credibility, it's early-stage—docs are roadmap-focused with starter kernels, tests exist but coverage is basic. Commit a year if portfolio-building trumps quick wins; otherwise, pair with official CUDA/Triton guides.

(198 words)

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