If you’ve already got a new M4 Mac and tried to run a macOS virtual machine on it, then you might have been disappointed. It seems that M4 chips can’t virtualise any version of macOS before 13.4 Ventura. So before you trade in or pass on your M1, M2 or M3 Mac, if you need access to older VMs, you might like to check whether this affects you.

I’m indebted to Csaba Fitzl for drawing my attention to this problem, and for reporting it to Apple in Feedback FB15774587. It has also been reported as affecting UTM, and I believe affects all other macOS virtualisation software for Apple silicon.

The bug

Running a macOS VM for any version before 13.4 Ventura on an M4 Mac results in a black screen, and the VM fails to boot. This is true whatever settings are used in the virtualiser, even if it’s set to boot the VM in Recovery mode. It’s also true when that VM has been built on that Mac: although that appears to complete successfully, when first run that VM opens as a black screen and never proceeds with personalisation and setup.

Currently the only way to run a VM with macOS prior to 13.4 Ventura is to do so on a host with an M1, M2 or M3 chip.

Can this be fixed?

Unfortunately, as this bug prevents the VM from booting, there’s no reliable way to access its log to discover what’s going wrong there. There’s no sign of the failure in the host’s log either: the host appears to initialise its Virtio and other support normally, without errors or faults. After those, virtualisation processes on the host fall silent as they wait for the VM to start, which never happens.

There is a useful clue in Activity Monitor, though: in its CPU pane, despite being allocated multiple virtual cores, only one is seen to be active on the host. That implies the failure is occurring before the VM kernel boots the other cores, an event that occurs early during the kernel boot phase. Until that point, pre-boot phases and the kernel run on just a single CPU core.

macOS 13.4 updated iBoot to version 8422.121.1, so at first sight the VM could be failing when running older firmware. That doesn’t appear likely, as version 8422.121.1 was also installed in the 12.6.6 security update, so 12.7.x shouldn’t suffer this problem, but it does.

It thus appears most likely that this bug strikes in the early part of kernel boot, in which case the most feasible solution would be to fix the bug in macOS kernels prior to 13.4, and promulgate new IPSW image files for those. I suspect that’s very unlikely to happen, and as far as I’m aware it would be the first time that Apple has issued revised IPSWs.

Which macOS can you virtualise?

Support for lightweight virtualisation of macOS on Apple silicon Macs was still in progress in the first version of macOS to run on M1 chips, Big Sur. You therefore cannot create or run Big Sur VMs.

The first versions that can run in VMs are macOS 12 Monterey, although prior to 12.4 they can sometimes be a bit fractious. They also have major limitations, such as not supporting shared folders with the host.

This is 12.1 with its old System Preferences, running happily on an M3 Pro.

macOS Ventura should run well on M1, M2 and M3 hosts, and 13.4 and later on M4 hosts too.

macOS Sonoma should run even better on all current Apple silicon Macs, and delivers a much improved display with autoscaling. However, 14.2 and 14.2.1 don’t automatically support shared folders because of a bug that was fixed in 14.3.

macOS Sequoia is fully compatible, and adds support for iCloud Drive and some other Apple Account features, although still won’t run App Store apps. It may also fail to install the required extras to support Apple Intelligence.

Summary

• Currently, M4 Macs can only run VMs of macOS Ventura 13.4 and later, 14 Sonoma, and 15 Sequoia.
• M1, M2 and M3 Macs can run VMs of macOS Monterey 12.0.1 and later.
• macOS Big Sur 11 can’t be virtualised on Apple silicon.

Further information

Viable and virtualisation
Why macOS 14.2 & 14.2.1 VMs lose shared folders, and how to work around it

Postscript

I’m grateful for the suggestion that it might be possible to work around this problem by running the older VM on a single core. Although I did manage to do that on an M3 (the first time I have seen recent macOS running on just one core!), that fails just the same on an M4, I’m afraid.

One common but atypical situation for any M-series chip is running a macOS virtual machine. This article explores how virtual CPU cores are handled on physical cores of an M4 Pro host, and provides further insight into their management, and thread mobility across P core clusters.

Unless otherwise stated, all results here are obtained from a macOS 15.1 Sequoia VM in my free virtualiser Viable, with that VM allocated 5 virtual cores and 16 GB of memory, on a Mac mini M4 Pro running macOS Sequoia 15.1 with 48 GB of memory, 10 P and 4 E cores.

How virtual cores are allocated

All virtualised threads are treated by the host as if they are running at high Quality of Service (QoS), so are preferentially allocated to P cores, even though their original thread may be running at the lowest QoS. This has the side-effect of running virtual background processes considerably quicker than real background threads on the host.

In this case, the VM was given 5 virtual cores so they could all run in a single P cluster on the host. That doesn’t assign 5 physical cores to the VM, but runs VM threads up to a total of 500% active residency across all the P cores in the host. If the VM is assigned more virtual cores than are available in the host’s P cores, then some of its threads will spill over and be run on host E cores, but at the high frequency typical of host threads with high QoS.

Performance

There is a slight performance hit in virtualisation, but it’s surprisingly small compared to other virtualisers. Geekbench 6.3.0 benchmarks for guest and host were:

• CPU single core VM 3,643, host 3,892
• CPU multi-core VM 12,454, host 22,706
• GPU Metal VM 102,282, host 110,960, with the VM as an Apple Paravirtual device.

Some tests are even closer: using my in-core floating point test, 1,000 Mloops run in the VM in 4.7 seconds, and in the host in 4.68 seconds.

Host core allocation

To assess P core allocation on the host, an in-core floating point test was run in the VM. This consisted of 5 threads with sufficient loops to fully occupy the virtual cores for about 20 seconds. In the following charts, I show results from just the first 15 seconds, as representative of the whole.

When viewed by cluster, those threads were mainly loaded first onto the first P cluster (pale blue bars), where they ran for just over 1 second before being moved to the second cluster (red bars). They were then regularly switched between the two P clusters every few seconds throughout the test. Four cycles were completed in this section of the results, with each taking 2.825 seconds, so threads were switched between clusters every 1.4 seconds, the same time as I found when running threads on the host alone, as reported previously.

For most of the 15 seconds shown here, total active residency across both P clusters was pegged at 500%, as allocated to the VM in its 5 virtual cores, with small bursts exceeding that. Thus that 500% represents those virtual cores, and the small bursts are threads from the host. Although the great majority of that 500% was run on the active P cluster, a total of about 30% active residency consisted of other threads from the VM, and ran on the less active P cluster. That probably represents the VM’s macOS background processes and overhead from its folder sharing, networking, and other Virtio device use.

When broken down to individual cores within each cluster, seen in the first above and the second below, total activity differs little across the cores in the active cluster. During its period in the active cluster, each core had an active residency of 80-100%, bringing the cluster total to about 450% while most active.

In case you’re wondering whether this occurs on older Apple silicon, and it’s just a feature of macOS Sequoia, here’s a similar example of a 4-core VM running 3 floating point threads in an M1 Max with macOS 15.1, seen in Activity Monitor’s CPU History window. There’s no movement of threads between clusters.

P core frequencies

powermetrics, used to obtain this data, provides two types of core frequency information. For each cluster it gives a hardware active frequency, then for each core it gives an individual frequency, which often differs within each cluster. Cores in the active P cluster were typically reported as running at a frequency of 4512 MHz, although the cluster frequency was lower, at about 3858 MHz. For simplicity, cluster frequencies are used here.

This chart shows reported frequencies for the two P clusters in the upper lines. Below them are total cluster active residencies to show which cluster was active during each period.

The active cluster had a steady frequency of just below 3,900 MHz, but when it became the less active one, its frequency varied greatly, from idle at 1,260 MHz up to almost 4,400 MHz, often for very brief periods. This is consistent with the active cluster running the intensive in-core test threads, and the other cluster handling other threads from both the VM and host.

CPU power

Several who have run VMs on notebooks report that they appear to drain the battery quickly. Using the previous results from the host, the floating point test used here would be expected to use a steady 7,000 mW.

This last chart shows the total CPU power use in mW over the same period, again with cluster active residency (here multiplied by 10), added to aid recognition of cluster cycles. This appears to average about 7,500 mW, only 500 mW more than expected when run on the host alone. That shouldn’t result in a noticeable increase in power usage in a notebook.

In the previous article, I remarked on how power used appeared to differ between the two clusters, and this is also reflected in these results. When the second cluster (P1) is active, power use is less, at about 7,100 mW, and it’s higher at about 7,700 when the first cluster (P0) is active. This needs to be confirmed on other M4 Pro chips before it can be interpreted.

Key information

• macOS guests perform almost as well as the M4 Pro host, although multi-core benchmarks are proportionate to the number of virtual cores allocated to them. In particular, Metal GPU performance is excellent.
• All threads in a VM are run as if at high QoS, thus preferentially on host P cores. This accelerates low QoS background threads running in the VM.
• Virtual core allocation includes all VM overhead from the VM, such as its macOS background threads.
• Guest threads are as mobile as those of the host, and are moved between P clusters every 1.4 seconds.
• Although threads run in a VM incur a small penalty in additional power use, this shouldn’t be significant for most purposes.
• Once again, evidence suggests that the first P cluster (P0) in an M4 Pro uses slightly more power than the second (P1). This needs to be confirmed in other systems.
• powermetrics can’t be used in a VM, not unsurprisingly.

Previous article

Inside M4 chips: P cores

Explainer

Residency is the percentage of time a core is in a specific state. Idle residency is thus the percentage of time that core is idle and not processing instructions. Active residency is the percentage of time it isn’t idle, but is actively processing instructions. Down residency is the percentage of time the core is shut down. All these are independent of the core’s frequency or clock speed.

This article describes one change that has caught out some using macOS Sequoia, and considers what has changed in Sequoia Virtual Machines (VMs).

periodic has been removed

After many years of deprecation, the periodic scheduled maintenance command tool has been removed from macOS 15.0. In its heyday, periodic was responsible for running daily, weekly and monthly maintenance and housekeeping schedules including rolling the system logs. Over that time, macOS has been given other means for achieving similar ends. For example, logs are now maintained constantly by the logd service, and aren’t retained by age, but to keep the total size of log files fairly constant. I don’t think that Sonoma performed any routine maintenance using periodic.

If you use periodic, then the best option is to use launchd with a LaunchAgent or LaunchDaemon. If you’d prefer to use cron, that’s still available but is disabled in macOS standard configuration.

Sequoia VMs: AI

Sequoia VMs created from an IPSW image of Sequoia (rather than upgraded from Sonoma or earlier) running on Sequoia hosts are the first to gain access to iCloud features. Now that 15.1 has been released with AI, I’ve been trying to discover whether that can also be used in a VM. So far, my 15.1 VM has sat for hours ‘preparing’, but AI still hasn’t activated on it. I suspect that, for the present, AI isn’t available to VMs. If you have had success, please let me know.

Sequoia VMs: macOS builds

My test 15.1 VM has also behaved strangely. It was originally created in 15.0, updated successfully to 15.0.1, then to 15.1, where it was running build 24B83, the version released generally on 28 October. Later that week Software Update reported that a macOS update was available, and that turned out to be a full install of 15.1 build 24B2083, released on 30 October for the new M4 Macs. This VM is hosted on a Studio M1 Max!

Installation completed normally, and that VM now seems to be running the new build perfectly happily, although it hasn’t proved any help in activating AI.

Don’t be surprised if your 15.1 build 24B83 VMs behave similarly. If anyone can suggest why that occurred I’d be interested to know, as it’s generally believed that build 24B2083 has been forked to support only M4 models.

Over the last few years, the performance of disk images has been keenly debated. In some cases, writing to a disk image proceeds at a snail’s pace, but this has appeared unpredictable. Over two years ago, I reported sometimes dismal write performance to disk images, summarised in the table below.

This article presents new results from tests performed using macOS 15.0.1 Sequoia, which should give a clearer picture of what performance to expect now.

Methods

Previous work highlighted discrepancies depending on how tests were performed, whether on freshly made disk images, or on those that had already been mounted and written to. The following protocol was used throughout these new tests:

1. A 100 GB APFS disk image was created using Disk Utility, which automatically mounts the disk image on completion.
2. A single folder was created on the mounted disk image, then it was unmounted.
3. After a few seconds, the disk image was mounted again by double-clicking it in the Finder, and was left mounted for at least 10 seconds before performing any tests. That should ensure read-write disk images are converted into sparse file format, and allows time for Trimming.
4. My utility Stibium then wrote 160 test files ranging in size from 2 MB to 2 GB in randomised order, a total of just over 53 GB, to the test folder.
5. Stibium then read those files back, in the same randomised order.
6. The disk image was then unmounted, its size checked, and it was trashed.

All tests were performed on a Mac Studio M1 Max, using its 2 TB internal SSD, and an external Samsung 980 Pro 2 TB SSD in an OWC Express 1M2 enclosure running in USB4 mode.

Results

These are summarised in the table below.

Read speeds for sparse bundles and read-write disk images were high, whether the container was encrypted or not. On the internal SSD, encryption resulted in read speeds about 1 GB/s lower than those for unencrypted tests, but differences for the external SSD were small and not consistent.

Write speeds were only high for sparse bundles, where they showed similar effects with encryption. Read-write disk images showed write speeds consistently about 1 GB/s, whether on the internal or external SSD, and regardless of encryption.

When unencrypted, read and write speeds for sparse (disk) images were also slower. Although faster than read-write disk images when writing to the internal SSD, read speed was around 2.2 GB/s for both. Results for encrypted sparse images were by far the worst of all the tests performed, and ranged between 0.08 to 0.5 GB/s.

Surprisingly good results were obtained from a new-style virtual machine with FileVault enabled in its disk image. Although previous tests had found read and write speeds of 4.4 and 0.7 GB/s respectively, the Sequoia VM achieved 5.9 and 4.5 GB/s.

Which disk image?

On grounds of performance only, the fastest and most consistent type of disk image is a sparse bundle (UDSB). Although on fast internal SSDs there is a reduction in read and write speeds of about 1 GB/s when encrypted using 256-bit AES, no such reduction should be seen on fast external SSDs.

On read speed alone, a read-write disk image is slightly faster than a sparse bundle, but its write speed is limited to 1 GB/s. For disk images that are more frequently read from than written to, read-write disk images should be almost as performant as sparse bundles.

However, sparse (disk) images delivered weakest performance, being particularly slow when encrypted. Compared with previous results from 2022, unencrypted write performance has improved, from 0.9 to 2.0 GB/s, but their use still can’t be recommended.

Performance range

It’s hard to explain how three different types of disk image can differ so widely in their performance. Using the same container encryption, write speeds ranged from 0.08 to 3.2 GB/s, for a sparse image and sparse bundle, on an external SSD with a native write speed of 3.2 GB/s. It’s almost as if sparse images are being deprecated by neglect.

Currently, excellent performance is also delivered by FileVault images used by Apple’s lightweight virtualisation on Apple silicon. The contrast is great between its 4.5 GB/s write speed and that of an encrypted sparse image at 0.1 GB/s, a factor of 45 when running on identical hardware.

Recommendations

• For general use, sparse bundles (UDSB) are to be preferred for their consistently good read and write performance, whether encrypted or unencrypted.
• When good write speed is less important, read-write disk images (UDRW) can be used, although their write performance is comparable to that of USB 3.2 Gen 2 at 10 Gb/s and no faster.
• Sparse (disk) images (UDSP) are to be avoided, particularly when encrypted, as they’re likely to give disappointing performance.
• Encrypting UDSB or UDRW disk images adds little if any performance overhead, and should be used whenever needed.

Previous articles

Introduction
Tools
How read-write disk images have gone sparse