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
My only experience with VMs on macOS is colima+docker, and it's relatively painful and inefficient (but usable).
https://github.com/trycua/cua/tree/main/libs/lume had a interesting take on this.
> 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?
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 :)
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.I was hoping to see the bare macOS with all the applications removed as much as possible, no graphical user interface, just the bare minimum to boot, login as a user, and write hello world dot txt with a text editor. Or maybe some command line apps? Or is it no longer macOS at that point?
Kind of a random question, but would it be feasible to intune enroll a macOS VM as a personal device?
I am so curious why no one make an env for agent specfic for macOS. Like the agent spawn in mac env
Is is possible to run macos on pc? Or at least dev in some way on PC for the mac.
I'm wondering if the Xcode simulator (without Xcode running) performs as well, my 2020 Intel MacBook Air has been incapable of running Safari in iOS smoothly for nearly all its life.
"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-...
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>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).