Performance tests are conducted using specific computer systems and reflect the approximate performance of MacBook Pro.

Open source project built with pre-release Xcode 12.2 with Apple Clang 12.0.0, Ninja 1.10.0.git and CMake 3.16.5.

Testing conducted by Apple in October 2020 using pre-production 13-inch MacBook Pro systems with Apple M1 chip, as well as production 1.7GHz quad-core Intel Core i7–based 13-inch MacBook Pro systems, all configured with 16GB RAM and 2TB SSD.

Comparison made against high-performing CPUs for notebooks and desktops commercially available at the time of testing. Performance per watt refers to the ratio of peak CPU performance to average power consumed using selected industry-standard benchmarks.

Testing conducted by Apple in October 2020 using pre-production 13-inch MacBook Pro systems with Apple M1 chip and 16GB of RAM.

*Listed pricing is Maximum Retail Price (inclusive of all taxes).

For more information, visit the Apple Store for Education.

‡Education pricing is available to current and newly accepted university students, parents buying for university students, and teachers and staff at all levels.

Have a question? Call a Specialist or chat online. ML accelerators The two dedicated ML accelerators are built for fast matrix multiplication, executing up to 1 trillion operations per second - perfect for low-latency ML tasks that don’t require the massive power of the Neural Engine. The CPU, GPU and Neural Engine are each designed for different types of machine learning workloads - and the ML performance controller distributes ML tasks to the right places to maximise performance. Optimised for machine learning The entire M1 chip is engineered for machine learning. And that data is processed on device to protect your privacy - all at lightning speed.ġ6-core Neural Engine The dedicated 16-core Neural Engine in the M1 chip can execute up to a staggering 11 trillion operations per second, powering workflows you couldn’t imagine before - like enabling the djay Pro AI app to isolate instrumentals and vocals of any song in real time. Machine learning lets apps build and apply models based on massive amounts of data - to do things like identify friends and family in photos and videos, interpret natural language for dictation and even analyse audio to recognise laughter, applause and more.

The magic of machine learning The machine learning technologies in the M1 chip open up a world of possibilities for Mac apps.

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