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 MacBook Air systems with Apple M1 chip and 8-core GPU, as well as production 1.2GHz quad-core Intel Core i7–based MacBook Air systems, all configured with 16GB RAM and 2TB SSD.Performance tests are conducted using specific computer systems and reflect the approximate performance of MacBook Pro. Comparison made against the highest-performing CPUs for notebooks, commercially available at the time of testing. Testing conducted by Apple in October 2020 using pre-production 13-inch MacBook Pro systems with Apple M1 chip and 16GB of RAM measuring peak single thread performance of workloads taken from selected industry standard benchmarks, commercial applications and open source applications.Performance tests are conducted using specific computer systems and reflect the approximate performance of MacBook Air.
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Tested with pre-release Final Cut Pro 10.5 using a 55-second clip with 4K Apple ProRes RAW media, at 4096x2160 resolution and 59.94 frames per second, transcoded to Apple ProRes 422. 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.
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.
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. And that data is processed on device to protect your privacy - all at lightning speed. 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.
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The magic of machine learning The machine learning technologies in the M1 chip open up a world of possibilities for Mac apps. These e‑cores are the most efficient place to run lightweight tasks, allowing the performance cores to tackle the most demanding workloads. And create stunning images in Adobe Lightroom with unprecedented speed and responsiveness.įour efficiency cores Four efficiency cores deliver outstanding performance for everyday tasks using just a tenth of the power. Record, mix and master professional-quality music in Logic Pro with scores of tracks, plug‑ins and virtual instruments. Compile in Xcode in significantly less time. It combines four performance cores and four efficiency cores that work together to tackle demanding multi-threaded tasks, resulting in a quantum leap in performance at a fraction of the power - and a significant boost to battery life.įour performance cores Our high-performance core is the world’s fastest CPU core when it comes to low-power silicon 3 - and M1 has four of them combining their efforts for a massive boost in performance. 8-core CPU The 8-core CPU in M1 is the highest-performing CPU we’ve ever built, by far.
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