Can Apple's M1 help you train models faster and cheaper than Nvidia's V100?

• mxnet

  4 20,644 4.1 C++

  Discontinued Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

  

> But you still lose something, e.g. if you use half precision on V100 you get virtually double speed, if you do on a 1080 / 2080 you get... nothing because it's not supported.

That's not true. FP16 is supported and can be fast on 2080, although some frameworks fail to see the speed-up. I filed a bug report about this a year ago: https://github.com/apache/incubator-mxnet/issues/17665

What consumer GPUs lack is ECC and fast FP64.

• tinygrad

  58 17,800 9.7 Python

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George hotz got his "for play" tensor library[a] to run on the Apple Neural Engine (ANE). The results were somewhat dissapointing, however.

[a]: https://github.com/geohot/tinygrad

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