I read the llama.cpp source code. Sixty thousand lines of C++ that single-handedly made local LLM inference possible on a laptop. This isn't "best practices from a textbook" — it's code where every line is responsible for keeping matrix multiplication inside the L2 cache and off the RAM bandwidth budget. I write PHP. A language where every value is wrapped in a zval, every object carries a 30+ byte header, and any foreach allocates a hash iterator. The comparison is unfair by definition. But I got curious: which of llama.cpp's tricks would even survive the transplant? And what would happen when I pushed the dataset to a billion records? I built a benchmark suite. Six optimizations from llama.cpp, translated to PHP 8.4 with JIT. Real numbers, statistical methodology, p99 latencies. Then I scaled the input from 1 million to 1 billion records, to see where the tricks stop being nice-to-haves and become the only path on which the code can finish. Half of my hypotheses were wrong. That's the actual story.…