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MacPaw Research says running language models entirely on Apple’s Neural Engine reduced energy use and latency for certain 8-bit tasks on M-series chips, compared with GPU inference using MLX.
Why it mattersThe results cover the LFM2.5-2.6B model on M2 Pro and M4 Pro. For longer context windows, the researchers say splitting execution between components can help offset rising attention costs on the Neural Engine. That makes the Neural Engine an interesting option for some local AI workloads, not a universal GPU replacement. The useful question is where the efficiency gains hold up beyond these tests.
Discuss: For local AI, would you prioritise lower energy use on shorter tasks or better performance as context windows grow?
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