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design

ai-performance-engineering: Optimize AI

Get GPU optimization and full-stack tuning resources for founders, with 1.6k+ GitHub stars
1,630 stars230 forksPythonQuality 8/10Updated 7/1/2026100% free ยท open source
What it does

The ai-performance-engineering tool provides resources and labs for optimizing AI system performance, including GPU optimization, distributed training, and full-stack tuning, specifically for founders

Install / run
git clone https://github.com/cfregly/ai-performance-engineering.git
When to use it
  • โ€ขWhen you need to optimize the performance of your AI models for production
  • โ€ขWhen you're experiencing bottlenecks in your AI pipeline and need to scale
  • โ€ขWhen you want to improve the efficiency of your AI system's GPU usage
Quick start
  1. 1Explore the labs directory for hands-on exercises, such as lab-01-gpu-acceleration
  2. 2Run the command `python lab-01-gpu-acceleration/lab.py` to start the first lab
  3. 3Review the Code directory for examples of optimized AI system code, including gpu_optimization.py
  4. 4Use the resources directory to find additional information on AI system performance engineering, such as the O'Reilly book
  5. 5Modify the config files, such as config.json, to customize the optimization settings for your AI system
Ready-to-paste prompt
Run `python lab-02-distributed-training/lab.py --num-gpus 4` to start the distributed training lab with 4 GPUs
Heads up: Make sure you have the necessary GPU drivers and CUDA version installed, as specified in the README, to run the GPU optimization labs
Saves to your device
What's inside โ€” free to inspect
No purchase needed

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6
top-level files
6
folders
2760.6M
repo size
Apache-2.0
license
Key files
README.md
File tree
.claude/
code/
docs/
img/
meetups/
resources/
.env.sample
.gitignore
.gitmodules
CONTRIBUTING.md
LICENSE
README.md
Quick Actions
Details
Creator
cfregly
Language
Python
Category
design
Published
4/21/2025

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