I'm not sure if he was talking about the ML engine, the ARM cores, the microcode, the library or the OS. But it does indeed have FP16 in the Arm cores.
> The Core ML runtime dynamically partitions the network graph into sections for the Apple Neural Engine (ANE), GPU, and CPU, and each unit executes its section of the network using its native type to maximize its performance and the model’s overall performance. The GPU and ANE use float 16 precision, and the CPU uses float 32.
I should have been more clear. I didn't mean the hardware, but the speedup you get from using mixed precision in something like Tensorflow with an NVIDIA GPU.
> The Core ML runtime dynamically partitions the network graph into sections for the Apple Neural Engine (ANE), GPU, and CPU, and each unit executes its section of the network using its native type to maximize its performance and the model’s overall performance. The GPU and ANE use float 16 precision, and the CPU uses float 32.
Also, this exploration (https://tlkh.dev/benchmarking-the-apple-m1-max#heading-neura...) reports the 5.1-5.3 TFLOPS FP16 ballpark performance.