07 01

A few weeks ago, Apple released its first custom-designed silicon chip for the Mac, the M1.

There have been several impressive benchmarks around its performance relative to its Intel-based predecessors, but we were interested in putting it through its paces on a ML (computer vision) workload.

While Apple announced support for TensorFlow training on the M1, the toolchain isn’t quite ready yet.

With some effort, we were able to get Jupyter notebooks running on Apple Silicon, for example, but the pre-release version of TensorFlow for Mac wasn’t ready for primetime just yet.

To compare performance, we used Create ML to tackle a no code object recognition problem.

We monitored this on all three computers for the full 5,000 iterations/epochs.

The results indicated that; (1)The Intel Core i5 took 542 minutes to run through 5,000 iterations (CPU training), (2)The Apple M1 took 149 minutes  to do the same (8% GPU utilization) and (3)The Intel Core i9 with Radeon Pro took 70 minutes (100% GPU utilization).

Based on this benchmark, the Apple M1 is 3.64 times as fast as the Intel Core i5 but is not fully utilizing its GPU and, thus, underperforms the i9 with discrete graphics.

It looks like there are still significant software optimizations for Apple to make in CreateML to fully take advantage of the raw power present in the M1.

It should eventually be able to fully utilize its GPU during just like the i9 was. At which point it would likely handily outperform it.

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