Print all known information about the GPU on Apple-designed chips

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Apple GPU Info

This is a mini-framework for querying parameters of an Apple-designed GPU. It also contains a command-line tool, gpuinfo, which reports information similarly to clinfo. It was co-authored with an AI.


Listed parameters:

  • Name
  • Core count
  • Clock frequency
  • Bandwidth
  • FLOPS (FP32 operations per second)
  • IPS (shader instructions per second)
  • System-level cache
  • Memory
  • Family


  • Swift module
  • C bindings
  • Command-line tool

Recognized devices:

  • A7 - A16
  • M1 - M1 Ultra
  • M2 - M2 Max
  • Future devices treated like the closest existing analog (e.g. A17 like A16)


One way to use this library is from the command-line:

git clone
cd applegpuinfo
swift run gpuinfo list

# Sample output
GPU name: Apple M1 Max
GPU core count: 32
GPU clock frequency: 1.296 GHz
GPU bandwidth: 409.6 GB/s
GPU system level cache: 48 MB
GPU memory: 32 GB
GPU family: Apple 7

You can also use it directly from Swift:

// Inside package manifest
dependencies: [
  // Dependencies declare other packages that this package depends on.
  .package(url: "", branch: "main"),

// Inside source code
import AppleGPUInfo

let device = try GPUInfoDevice()


Original Goal: In one hour, finish a mini-package and command-line tool for querying Apple GPU device parameters.

Results: I spent 57 minutes finishing the file that wraps the AppleGPUDevice structure. I asked GPT-4 to generate the tests and command-line tool. I renamed the command-line tool from applegpuinfo to gpuinfo according to the AI's suggestion. Finally, I congratulated it and asked for it to leave a comment to users on the README. That triggered a safeguard and it quit the conversation. The stop time was 1 hour, 25 minutes.

Documentation of AI contributions:

After creating the first release of the library, I have continued experimenting with workflows accelerated by free access to GPT-4. The above document details these subsequent modifications to the library.


This framework is confirmed to work on the following devices. If anyone wishes to contribute to this list, please paste the output of gpuinfo into a new GitHub issue. Different variations of the same chip (e.g. different cores or memory) are welcome.

Production Year Chip Cores SLC Memory Bandwidth TFLOPS
2017 A10X 12 0 MB 4 GB 68.2 GB/s 0.768
2021 A15 5 32 MB 5.6 GB 34.1 GB/s 1.789
2021 M1 Pro 16 24 MB 32 GB 204.8 GB/s 5.308
2021 M1 Max 32 48 MB 32 GB 409.6 GB/s 10.617
2022 M1 Ultra 48 96 MB 64 GB 819.2 GB/s 15.925
2023 M2 Pro 19 24 MB 32 GB 204.8 GB/s 6.800


This project was made possible by GPT-4, accessed through Bing Chat.


  • Swift Tools 5.7.0
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Last updated: Tue May 30 2023 09:29:17 GMT-0500 (GMT-05:00)