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  • I think I got the smallest:

        $ podman image list | grep cross
    docker.io/gotson/crossbuild            latest      d96ea9b7054b  3 years ago   6.71 GB
    used to cross-build to darwin.
    by rurban
  • Honestly macOS probably can go much lower than that if you turn off some stuff that's not strictly necessary for a VM. The first iPhones only had 128 MiB of RAM and they ran a trimmed down version of macOS Tiger I believe. It's just that RAM has been quite abundant so far, so there was no real reason to try to trim it down, but it's definitely possible, and probably not that hard either, we just need to start trying again :)
    by nasretdinov
  • > Starting with 4 virtual cores and 8 GB vRAM, where the VM ran perfectly briskly with around 5 GB of memory used

    But... if you start applications inside your VM it will want the full 8 Gb you've allocated not the 5 Gb it uses at startup?

    by nottorp
  • https://github.com/trycua/cua/tree/main/libs/lume had a interesting take on this.
    by dhruv3006
  • My only experience with VMs on macOS is colima+docker, and it's relatively painful and inefficient (but usable).
    by mgaunard
  • "We might hope that macOS would process AI tasks using the CPU and GPU rather than the neural engine, when running in a VM."

    That specific Geekbench test is to measure the ANE performance, which they did by setting the CoreML run to cpuAndNeuralEngine. They could have set it to all and it would use any hardware available, but that would be counterproductive to a test that hopes to measure the ANE, no?

    And note that there is no "just ANE" option. In this case it is probably the virtualized CPU side of the equation that's yielding the massive slowdowns for int8 and quantized runs.

    The ANE isn't the problem here.

    https://dennisforbes.ca/blog/microblog/2026/02/apple-neural-...

    by llm_nerd
  • Got a M5 air recently - my first dive into MacOS land so trying to figure this out too.

    Seems essentially impossible to get:

    * pytorch

    * GPU acceleration

    * VM/container like isolation

    The virtio-gpu layer gets closest but seems to only pass through graphics GPU not compute GPU so no pytorch

    by Havoc
  • >Starting with 4 virtual cores and 8 GB vRAM, where the VM ran perfectly briskly with around 5 GB of memory used, I stepped down to 3 cores and 6 GB, to discover that memory usage fell to 3.9 GB and everything worked well. With just 2 cores and 4 GB of memory only 3.1 GB of that was used, and the VM continued to handle those lightweight tasks normally.

    Good reminder that there's a certain amount of memory tied up with each core (probably mainly page cache and concurrency handling etc).

    by fouc

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