I Asked Claude Fable 5 to Improve llama.cpp.. and It Did

I pointed Fable Anthropic's new model at llama.cpp, the engine that runs local LLMs on consumer hardware, and asked one thing: make it faster. It made prefill ~64% faster on the exact same GPU. No new hardware, same model, token-identical output. Running a 35B mixture-of-experts model (Qwen3.6 35B-A3B) on a 12GB RTX 3060 means the experts live in system RAM and stream across PCIe every prefill pass — and that bus, not the GPU, is the real bottleneck. Fable found it, wrote the patches, benchmarked every change itself, and even caught a bug in its own code. Four optimizations. Two worked, two didn't — and I show you exactly why, including the one that ran 14× slower and lost to my hardware, not the AI. The 64.5% is the honest CODE gain (1143 → 1880 tokens/sec), kept separate from config tuning. All the patches are on my public GitHub fork so you can test them on your own rig. This is what taking back control of local AI actually looks like: the tools we build AI with, starting to improve themselves — on a desk, not in a frontier lab. ⏱️ CHAPTERS 0:00 I Used Fable to Improve llama.cpp 0:52 The Free Hand 2:50 Where the Math Happens 5:54 The Dead Switch (Win #1, +21%) 8:18 The Overlap (the headline — 41.7% → 2.8% idle) 11:10 The Second Front (two honest failures) 13:55 The Verdict 15:04 The Flywheel 🔗 GitHub fork (all the patches): github.com/thecodacus/llama.cpp 🛠️ Rig: RTX 3060 12GB · Ryzen 5 5600X · 32GB DDR4 · Ubuntu 24.04 · Qwen3.6 35B-A3B, --n-cpu-moe 26 💬 What would YOU point Fable at? Drop it in the comments. Local AI you actually own — subscribe. #llamacpp #claudefable5 #fable #localai #localllm #ai #llm