When you think of programming machine-learning PCs, hard-core developers dreams turn to high-priced powerhouse machines running Linux. But a recent benchmark by TensorFlow programmers showed that Macs powered by Apple’s new M1 chip can give these boxes a run for their money.
Some friends and I were recently talking about how much computing power you really need these days. After all, even a relatively low-powered Chromebook can handle all your office work. But, I reminded them, serious programmers working on machine learning (ML) need all the CPU horsepower they can get. A typical entry-level ML workstation, such as the HP Z6 G4 Workstation comes with a single 28 core,2.0 GHz Intel Xeon Platinum,48GBs of RAM, a 500GB 2.5″ SSD, and an NVIDIA GeForce RTX 2080Ti running Ubuntu Linux 20.04. An Apple Mac? Surely you jest! Well, actually, no it’s not a joke. Pankaj Kanwar, Google’s Tensor Processing Units (TPU) Technical Program Manager, found that TensorFlow on Macs powered by Apple’s new M1 chip kicks rump and takes names. Also: My biggest Apple M1 question: What’s Intel been doing all these years? TensorFlow, which Google created, is the most popular open-source ML platform.
