Apple’s New M1 Chip is a Machine Learning Beast
By: Daniel Bourke
Tags: Apple, M1, Machine Learning, Apple Silicon, Editors Pick

I watched the keynote and saw the graphs, the battery life, the instant wake. And they got me. I started to think, how could one of these new M1-powered MacBooks make their way into my life?

Of course, I didn’t need one but I kept wondering what story could I tell myself to justify purchasing another computer? Then I had it. My 16-inch MacBook Pro is too heavy to carry around all the time. Yeah, that’ll do. This 2.0 kg aluminium powerhouse is too much to be galavanting.

Wait… 2.0 kg, as in, 4.4 pounds?

That’s it?

Yes.

Wow. It’s not even that heavy.

C’mon now… let’s not let the truth get in the way of a good story.

I had it. My reason for placing an order on a shiny new M1 MacBook (or two). My 16-inch MacBook is too heavy to lug around to cafes and write code, words, edit videos and check emails sporadically.

And Apple seems to think their new M1 chip is 11x, 15x, 12x, 3x faster on a bunch of different things. Thought-provoking numbers but I’ve never measured any of these in the past.

All I care about is: can I do what I need to do, fast.

The last word of the previous sentence is the most important. I’ve become conditioned. Speed is a part of me now. Ever since the transition from hard drives to solid-state drives. And I’m not going back.

I bought the 16-inch in February 2020. I’d just completed a large project and was flush with cash, so I decided to future proof my work station. Since I edit videos daily and hate lag, I opted for the biggest dawg I could buy and basically maxed everything except for the storage (see the specs below).

Thankfully I’ve still got a friend at Apple who was able to apply their employee discount to the beast (shout out to Joey).

Anyway, we’ve discussed my primary criteria: speed. Let’s consider the others:

Speed. If it’s not fast, get lost. Cost. A big factor but I didn’t mind paying for the higher spec machine nor do I mind paying for a quality computer. It’s my primary tool. I use it to make art, I use it to make money, I use it to learn, I use it to communicate to the world. Portability. I don’t like sitting in an office all day. Can I take this thing to a cafe or library for a few hours without searching for a power outlet? Consider portability a combination of battery life and weight.

Why test/compare them

Why not?

But really, I’m a nerd. And an Apple fan. Plus, I wanted to see how my big-dawg-almost-top-of-the-line 16-inch MacBook Pro faired against the new M1 chip-powered MacBook’s.

Plus, I can’t remember being this excited for a new computing device since the original iPhone.

Other reasons include: carrying around a lighter laptop and tax benefits (if I buy another machine before the end of the year, I can claim it on tax).

Mac specs

Whenever I buy a new machine, I usually upgrade the RAM and the storage at least a step or two from baseline.

512GB storage and 16GB RAM seems to be the minimum for me these days (seriously, who is running a 128GB MacBook effectively?).

So for the M1 MacBook’s, I upgraded both of their RAM from 8GB to 16GB and for the 13-inch Pro, I upgraded from 256GB to 512GB storage.

The 16-inch MacBook is my current machine, which I’ve never had a problem with until running the tests below.

Specifications of each of the Macs tested. Note: there are cheaper configurations of each but I typically upgrade the RAM and storage on each of my machines.

*Price (actual) is the actual price I paid for each model. Note for the MacBook Pro 16-inch, I actually paid ~$5,500AUD since I have a friend who works at Apple and applied his employee discount (thank you Joey).

**Price (baseline) is the price you’d pay if you upgraded all processing components (e.g. 8GB -> 16GB RAM on the M1 models and 2.3GHz -> 2.4GHz on the Intel model) except storage (since storage is usually the most expensive upgrade).

The tests

Apple’s graphs were impressive. And the GeekBench scores were even more impressive. But these are just numbers on a page to me. I wanted to see how these machines performed doing what I’d actually do day-to-day:

Writing words. I assume they all perform well at this.

I assume they all perform well at this. Browsing the web. Same as above.

Same as above. Editing videos. One of the primary uses I was interested in and one of the main reasons I bought the 16-inch MacBook Pro with dedicated GPU.

One of the primary uses I was interested in and one of the main reasons I bought the 16-inch MacBook Pro with dedicated GPU. Writing code. A text editor doesn’t require much but Xcode is getting pretty hefty these days.

A text editor doesn’t require much but Xcode is getting pretty hefty these days. Training machine learning models. I write a lot of machine learning code. I don’t expect to be able to train state-of-the-art models on a laptop but at least being able to tweak things/experiment would be nice.

Reflecting on the above, I devised three tests:

Video exporting with Final Cut Pro. I made a pretty hefty video earlier in the year (2020 Machine Learning Roadmap), it’s 2 hours, 37 minutes+ long. So I figured it’ll be cool to see how long each machine takes to export it. Machine Learning Model training with CreateML. Apple’s black-box machine learning model creation app. I don’t any large Xcode files handy but I decided to see how the CreateML app handles training machine learning models on the new silicon. Native TensorFlow code using tensorflow_macos . The test I was most excited for. Apple and TensorFlow published a blog post saying the new TensorFlow for macOS fork sped up model training dramatically on the new M1 chip. Are these claims true?

Why not test more extensively?

These are enough for me. I’ve got other sh*t to do.

Alright, time for the results. The best results for each experiment have been highlighted in bold.

Experiment 1: Final Cut Pro video export

For this one, all machines were given ample time to pre-render the raw footage. So when the export button got clicked, they all should’ve been relatively on the same page.

Experiment details:

Video length: 2 hours, 37 minutes+

2 hours, 37 minutes+ Export file size: 26.6GB (quoted), ~6.5GB (actual)

No surprise here, the 16-inch MacBook Pro exported in the fastest time. Most likely because of the dedicated 8GB GPU or 64GB of RAM.

However, it seems using the dedicated GPU came at the cost of battery life drain and fan speed (in the video you can hear the fans going off like a jet).

During the video export, the M1-powered MacBook Air and MacBook Pro 13-inch remained completely silent (the MacBook Air had no choice, it doesn’t have a fan but the MacBook Pro 13-inch’s fan never turned on).

Experiment 2: CreateML machine learning model training

I’ve never actually used a machine learning model trained by CreateML. However, I decided to see how one of Apple’s custom apps would leverage their new silicon.

For this test, each MacBook was setup with the following CreateML settings: