elizabetht

elizabetht / 100-days-of-inference

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100 days of LLM inference engineering — daily posts, experiments, and visualizations

12
4
69% credibility
Found Apr 02, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

A personal 100-day educational challenge documenting experiments and notebooks on optimizing AI language model performance across runtime, scaling, tooling, and production techniques.

How It Works

1
🕵️ Discover the Learning Adventure

You come across this personal 100-day challenge where someone dives deep into making AI generate text faster and smarter, inspired by a special book.

2
📖 Explore the Big Plan

You read the clear roadmap divided into phases, from perfecting basics on one powerful computer to handling massive real-world demands.

3
🚀 Jump into Day One

You open the first lesson's guide and examples, watching step-by-step how AI builds responses one word at a time without any fancy tricks.

4
💡 Grasp the Core Ideas

You uncover why longer replies take more time and get excited about all the clever ways to make it quicker and smoother.

5
✨ Unlock More Lessons

You use a handy guide to create notebooks for upcoming days, keeping the learning journey going at your own pace.

6
🛠️ Build and Test Hands-On

You roll up your sleeves for projects like recreating AI basics from scratch and experimenting on powerful home setups.

🎉 Master AI Speed Secrets

You finish the challenge feeling empowered, knowing how to run AI chats reliably, affordably, and at scale for real use.

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AI-Generated Review

What is 100-days-of-inference?

This Jupyter Notebook repo delivers a 100-day hands-on challenge for mastering LLM inference engineering, from CUDA kernels and quantization to Kubernetes autoscaling and multi-modal serving. It tackles the pain of deploying generative AI models in production—making them faster, cheaper, and reliable—through self-contained, runnable experiments with benchmarks and visualizations, all tested on a home-lab NVIDIA DGX cluster. Think daily posts like a "100 days wild" survival series, but for inference: baseline mechanics today, speculative decoding tomorrow.

Why is it gaining traction?

It stands out with a book-backed curriculum (*Inference Engineering*, 2026) that spans runtime tweaks like vLLM PagedAttention to production stacks with Prometheus metrics and Grafana dashboards, plus from-scratch builds like custom tokenizers and KV caches. Developers hook on the CLI-driven notebook generation (`/learn-inference-eng next`) for jumping into topics like TensorRT-LLM or multi-cloud routing, skipping generic tutorials for real throughput benchmarks. Unlike scattered GitHub 100 projects or rust 100 exercises, it's a systematic path with home-lab proofs.

Who should use this?

ML engineers scaling LLM serving on GPU clusters, AI infra leads optimizing TTFT and TBT for high-traffic apps, or backend devs building FastAPI inference servers with async clients. Ideal for teams hitting GitHub 100 MB limits on model artifacts or debugging cold starts in daily production pipelines.

Verdict

Watchlist for inference pros—solid outline and Day 1 baseline demo shine, but at 12 stars, 0.699999988079071% credibility score, and 1/100 progress, it's raw and home-lab specific; fork and contribute to mature it beyond the hype of 100 days my prince ende.

(178 words)

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