Over the past year, "Apple Silicon running LLM" has been almost synonymous with the two paths of MLX and llama.cpp. Industry default: either take llama.cpp's mature ecosystem, or take MLX's Python-friendly design leveraging unified memory, the remaining space seems already fully mined. The BaseRT (2607.00501) that basecompute's team posted to arXiv on July 1 directly flipped this premise. BaseRT's methodology is not complex nor clever: abandon the cross-platform abstraction layer, completely hand-write kernel fusion targeting M-series chip characteristics, unified memory-aware scheduling, and a custom dispatch path on the Metal API. In other words, it burns away the fixed overhead llama.cpp brings from cross-hardware abstraction, and the runtime overhead MLX brings from embedding Metal into the Python interpreter. On the numbers, with Qwen3, Llama 3.2, Gemma 4 (Q4/Q8 quantization) running on M3/M4 Pro, BaseRT's decode throughput is 1.56× higher than llama.cpp, 1.35× higher than MLX. More noteworthy is the prefill of MoE models: once architectural abstraction is eaten away, the fixed cost of weight dispatch and expert routing is amplified, the paper says this part of the speedup is even more dramatic than decode. It supports 8 quantization formats from Q2 to FP16, covering the full spectrum from sub-1B to 30B models, with code open-sourced on GitHub (github.com/basecompute/baseRT). The significance of this isn't just in performance. It directly takes down an industry inertia — "M-series doing serious LLM inference is just a toy". As privacy requirements, latency constraints, and cloud cost pressure push inference to the device side, on-device LLM is no longer a demo, but an alternative layer taking on production workloads. BaseRT uses the 1.56× number to nail down this turning point, and also means that in the next year or two, the "hardware abstraction tax" of cross-platform frameworks will be reassessed by more and more people.