LLaMA.cpp Patch Delivers 40% Faster Qwen 27B Inference on M5 Max

Qwen 3.6 27B on a MacBook Pro M5 Max 64GB hitting 34tokens per sec, locally with atomic[.]chat 90% acceptance rate, i.e. most draft tokens matched what the main model would have produced, so the speed gain is not from skipping quality checks, but from avoiding repeated full-cost decoding work. TurboQuant and GGUF handle the storage and runtime side: the model is compressed enough to run locally, while llama.cpp can feed Apple Silicon efficiently instead of waiting on huge weight movement. Pretty serious local-inference result, changes what “laptop AI” can feel like.