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The Mac Studio is quite a powerful machine at a relatively reasonable price.

by jb1991 4y ago

ChuckNorris89 4y ago
How many games run on it?
- davely 4y ago
  Quite a lot, at least based on my experience using Steam and playing various games I’ve bought for my actual gaming PC, but have builds available for multiple platforms. As long as they’re 64 bit builds, MacOS’s Rosetta translation layer does a great job of running then without a hitch. Apple Silicon is a beast. I wish more developers would take advantage of it.
  - krisroadruck 4y ago
    This is only true if your primary game preference is casual games. The mac studio lacks a discrete graphics card. Ignoring the OS, the whole ARM vs x86, and Rosetta, wine, whatever stumbling blocks - just lack of a discrete GPU is enough to make it a no-go as a serious gaming rig. This isn't just me talking out of my neck either. I have 3 rigs I keep around my desk, and one of them is a mac studio. Great for dev and video editing, but for gaming not so much. Edit: "Why do you need 3 computers?" - I regularly switch between Windows, Ubuntu & MacOS, and I don't like dinking with switching my monitor inputs, dual booting or remoting in. Rather just swivel my chair. Yes I fully realize how ridiculous this is. Some people like nice cars. I like wasting money on computers apparently. ¯\_(ツ)_/¯
    - bitL 4y ago
      No worries, I have 12 computers around my desk...
  - amelius 4y ago
    > Apple Silicon is a beast. I wish more developers would take advantage of it. It's consumer electronics. Not very useful for people who want to build things containing a CPU/GPU.
    - jb1991 4y ago
      It’s a lot more than “consumer electronics”. When I upgraded my i7 last year for a base model M1 chip, I found that all of my c++ code ran vastly more quickly on a machine that cost about half as much as what it replaced. What is strange is that if you look at the raw specs, it should not have been near half in execution time —- there’s definitely some fascinating things going on in that chip. The unified memory architecture of Apple Silicon is particularly convenient for anyone building stuff using both a CPU and GPU.
- robertoandred 4y ago
  Computers are more than just dumb toys.
  - ChuckNorris89 4y ago
    Are you talking about Macs?
    - robertoandred 4y ago
      Talking about computers.
- jb1991 4y ago
  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...

The Mac Studio is quite a powerful machine at a relatively reasonable price.

by jb1991 4y ago

The Mac Studio is quite a powerful machine at a relatively reasonable price.

ChuckNorris89 4y ago

How many games run on it?

davely 4y ago
Quite a lot, at least based on my experience using Steam and playing various games I’ve bought for my actual gaming PC, but have builds available for multiple platforms. As long as they’re 64 bit builds, MacOS’s Rosetta translation layer does a great job of running then without a hitch. Apple Silicon is a beast. I wish more developers would take advantage of it.
- krisroadruck 4y ago
  This is only true if your primary game preference is casual games. The mac studio lacks a discrete graphics card. Ignoring the OS, the whole ARM vs x86, and Rosetta, wine, whatever stumbling blocks - just lack of a discrete GPU is enough to make it a no-go as a serious gaming rig. This isn't just me talking out of my neck either. I have 3 rigs I keep around my desk, and one of them is a mac studio. Great for dev and video editing, but for gaming not so much. Edit: "Why do you need 3 computers?" - I regularly switch between Windows, Ubuntu & MacOS, and I don't like dinking with switching my monitor inputs, dual booting or remoting in. Rather just swivel my chair. Yes I fully realize how ridiculous this is. Some people like nice cars. I like wasting money on computers apparently. ¯\_(ツ)_/¯
  - bitL 4y ago
    No worries, I have 12 computers around my desk...
- amelius 4y ago
  > Apple Silicon is a beast. I wish more developers would take advantage of it. It's consumer electronics. Not very useful for people who want to build things containing a CPU/GPU.
  - jb1991 4y ago
    It’s a lot more than “consumer electronics”. When I upgraded my i7 last year for a base model M1 chip, I found that all of my c++ code ran vastly more quickly on a machine that cost about half as much as what it replaced. What is strange is that if you look at the raw specs, it should not have been near half in execution time —- there’s definitely some fascinating things going on in that chip. The unified memory architecture of Apple Silicon is particularly convenient for anyone building stuff using both a CPU and GPU.
robertoandred 4y ago
Computers are more than just dumb toys.
- ChuckNorris89 4y ago
  Are you talking about Macs?
  - robertoandred 4y ago
    Talking about computers.
jb1991 4y ago
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...