[P] Bypassing CoreML to natively train a 110M Transformer on the Apple Neural Engine (Orion)

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It is hard to communicate how frustrating the current Apple ML stack is for low-level research. CoreML imposes opaque abstractions that prevent direct ANE programming and do not support on-device training. Despite having up to 38 TOPS (INT8) and ~19 TFLOPS of fp16 compute, the ANE remains almost entirely unused for large language model workloads. Building on the foundational hardware reverse-engineering by maderix (who mapped the private API surface and benchmarked the 32 MB SRAM cliff), I wa...