One signal

A new open-source project builds a small LLM inference engine from scratch in C++ and CUDA, with the aim of teaching how a model turns weights into generated tokens.

The repository describes custom kernels for embedding, normalisation and attention, plus direct model loading, a BPE tokenizer and both static and continuous batching. It also includes more than six hours of tutorials and 85 pages of handwritten notes. That makes it a practical route into the machinery behind local inference, rather than another black-box demo. Which would teach you more: building a small inference engine yourself, or starting with a production framework?

Discuss: Which would teach you more about LLM inference: building a small engine from scratch, or starting with a production framework?

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