Infatoshi

Infatoshi / physics-llm-inference

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Companion code for The Physics of LLM Inference book

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Found Feb 01, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Educational code companion to the book 'The Physics of LLM Inference', offering runnable examples, benchmarks, and tests organized by chapter to explore transformer mechanics, generation loops, optimizations, batching, and production serving.

How It Works

1
📚 Find the learning code

You discover this collection of hands-on examples that teach the inner workings of AI language models, like a workbook for a special book.

2
🛠️ Get everything ready

You follow the easy setup steps to prepare your computer, and soon your playground is open.

3
🔬 Build AI building blocks

You play with pieces like attention and feed-forward networks, watching a simple language model come alive.

4
📈 Test speeds and limits

You measure how fast different parts run, discovering why some tricks make everything quicker.

5
✅ Verify it all works

You run checks to confirm every example behaves just right.

🧠 Master AI generation secrets

Now you truly understand the physics of making AI chat fast and efficient, ready to build your own.

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AI-Generated Review

What is physics-llm-inference?

This Python repo is companion code for "The Physics of LLM Inference" book, delivering runnable benchmarks and toy models to dissect LLM serving bottlenecks. Run scripts to measure GEMM vs GEMV speeds, KV cache memory scaling, FlashAttention savings, or continuous batching throughput—reproducing the book's exact numbers on your GPU. It's a hands-on lab for grokking why inference hits compute/memory walls, from basic transformers to production tricks like paged attention.

Why is it gaining traction?

Unlike dense theory books or black-box frameworks, it pairs clear explanations with verifiable benchmarks using Torch and Triton, letting you tweak and profile real perf diffs (e.g., fused SwiGLU vs naive). Devs grab it as github companion code for the book, skipping vague slides for code that spits JSON timings matching published charts. Low-barrier quickstarts and full test suites make experimentation addictive.

Who should use this?

Inference engineers tuning vLLM/SGLang forks, researchers benchmarking MoE or chunked prefill, or startup devs building custom LLM APIs needing roofline analysis. Ideal for GPU hackers dissecting why decode stalls at batch=1 but flies at 32.

Verdict

Strong pick for learning LLM physics—MIT-licensed, pytest-covered, uv-managed deps—but 1.0% credibility score and 15 stars signal early days; pair with the book for context. Fork and benchmark your hardware today.

(178 words)

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