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I have no idea, in my long career I’ve never installed a computer game on anything. It might be surprising, but some people use computers for other things.

• ChuckNorris89 4y ago
  Good for you mate, but games were a main driver of hardware innovations both for CPUs and GPUs and were responsible for the adoption in the household of the PC and many people today still prefer them to the locked down console ecosystems because once your gaming session is over you can use that computing power for other things, like coding, rendering, etc. The fact you never installed a game is largely irrelevant.
  • jb1991 4y ago
    Hi Chuck, you mention hardware innovations but it's largely well-recognized that the deprecation of cross-platform GPU APIs is the main reason the mac is not known for gaming, and has nothing to do with whether or not it has a powerful hardware. It has, in fact, some of the most impressive innovations in recent memory, including unified memory and more. The Mac Studio is one of the most powerful desktops currently on the market, and its integrated GPU competes quite well against many discrete GPUs. It's your comment and subsequent argument about gaming that is ultimately irrelevant on the topic of whether or not it is "powerful". btw big fan of walker, texas ranger. Please do more like that one.
    • ChuckNorris89 4y ago
      And yet despite their powerful GPUs, Mac is still irrelevant in the GPU space due to their locked down nature to a niche API that's Apple exclusive. Everyone targets Nvidia or AMD GPUs for both gaming and compute like ML. Mac GPUs are mostly used by YouTubers to accelerate video editing.
      • jb1991 4y ago
        Many of the most important ML toolchains run natively on Apple Silicon, including PyTorch [0] and TensorFlow. For example the PyTorch folks have this to say about it [0]: > Every Apple silicon Mac has a unified memory architecture, providing the GPU with direct access to the full memory store. This makes Mac a great platform for machine learning, enabling users to train larger networks or batch sizes locally. This reduces costs associated with cloud-based development or the need for additional local GPUs. The Unified Memory architecture also reduces data retrieval latency, improving end-to-end performance. Plenty of other reports out there [1]: >We ran a sweep of 8 different configurations of our training script and show that the Apple M1 offers impressive performance within reach of much more expensive and less energy efficient accelerators such as the Nvidia V100 for smaller architectures and datasets. If one is looking for a great bang for the buck and a big savings in energy use, the GPUs in Apple Silicon are a compelling option. Plenty of Apple haters (presumably yourself) like to ignore what has been achieved with this technology, but that doesn't make it any less real. [0] https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ https://pytorch.org/blog/introducing-accelerated-pytorch-tra... [1] https://wandb.ai/vanpelt/m1-benchmark/reports/Can-Apple-s-M1-Help-You-Train-Models-Faster-Cheaper-Than-NVIDIA-s-V100---VmlldzozNTkyMzg https://wandb.ai/vanpelt/m1-benchmark/reports/Can-Apple-s-M1...
        • ChuckNorris89 4y ago
          And yet it won't run most games and apps because Apple won't support Vulkan like ... every single other computing platform in existence. You're ignoring mainstream apps and focusing on niche things off GitHub. What's Apple's market share again in the GPU compute space? Oh yeah, it's almost zero.
          • jb1991 4y ago
            PyTorch and TensorFlow are not niche, they are key tools in the ML field, which is what you brought up and I responded too.