AI without GPUs: Accessing Sapphire Rapids AMX instructions on vSphere

Full disclosure: I used to work for a startup called Bitfusion, and that startup was bought by VMware, so I now work for VMware. At Bitfusion we developed a technology for accessing hardware accelerators, such as NVIDIA GPUs, remotely across networks using TCP/IP, Infiniband, and PVRDMA. Although I still do some work on the Bitfusion product at VMware, I spend most of my time these days seeing what I can do on the vSphere platform using the latest AI/ML accelerator hardware from NVIDIA, Intel, and AMD.

Although I work at VMware, this is my own personal blog, and any views, opinions, or mistakes I publish here are purely my own and are not official views or recommendations from VMware.

This specific article is based on a talk I just gave at VMware Explore Las Vegas.

Everyone wants the latest, greatest GPUs for AI/ML training and inference workloads. As I’m sure most of you know, GPUs are just specialized matrix processors. They can quickly perform mathematical operations — in parallel — on matrices of numbers. Although GPUs were originally designed for graphics, it turns out that being able to do matrix math is extremely useful for AI/ML.

Unfortunately, every GPU vendor on the planet seems to be having about a one year order backlog when it comes to shipping datacenter-class GPUs. If you’re having a hard time buying GPUs, one thing you can do to increase the performance of your AI/ML workloads is to let the CPU’s AMX instructions do some of that AI/ML work, lessening the need for expensive and hard-to-procure GPUs.

Advanced Matrix Extensions (AMX) are a new set of instructions available on x86 CPUs. These instructions are designed to work on matrices to accelerate artificial intelligence and machine learning -related workloads. These instructions are beginning to blur the lines between CPUs and GPUs when it comes to machine learning applications.

When I started hearing that Intel Sapphire Rapids CPUs were embedding matrix operations in the CPU’s instruction set I started wondering what can I do with those instructions using AI/ML tools?

“We can do good inference on Skylake, we added instructions in Cooper Lake, Ice Lake, and Cascade Lake. But AMX is a big leap, including for training.”

— Bob Valentine, the processor architect for Sapphire Rapids


As you replace older hosts with Sapphire Rapids -based hosts you not only get performance improvements for traditional computing, you also get AMX capabilities for AI/ML workloads. You can execute diverse AI & non-AI multi-tenant workloads side by side in a virtualized environment. You have the flexibility to repurpose the IT infrastructure for AI and non-AI use cases as demand changes without additional capex. The ubiquity of Intel Xeon & vSphere in on-Prem and cloud environments, combined with an optimized AI software stack, allows you to quickly scale the compute in hybrid environments. You can run your entire end to end AI pipeline — data prep, training, optimization, inference – using CPUs with built-in AI acceleration.

Does this really work? What kind of workloads can I run?

Here’s a demo I did using an llm-foundry LLM with a 7B parameter model from HuggingFace. The code is installed in a container and the model is loaded in a Kubernetes volume. I first start the LLM in a Tanzu cluster on an Ice Lake CPU -based system with no GPUs. As you can see it takes a while just to load the model into memory, then when it starts it’s pretty jerky and slow.

I start the same exact container on Tanzu cluster running on a Sapphire Rapids CPU -based system with no GPUs. The hardware is roughly equivalent (both are using what would be considered mid-range servers at the time they were purchased), the VMs are equivalent in memory and vCPUs, but the Sapphire Rapids system runs much faster than the previous generation Ice Lake system.

LLM running on Sapphire Rapids with AMX

In addition to the above side-by-side comparison of an LLM running on Ice Lake vs Sapphire Rapids, we also fine-tuned an LLM using just Sapphire Rapids CPUs. Starting with an off-the-shelf LLAMA2-7B model, we fine-tuned it with a dataset “Finance-Alpaca” of about 17,500 queries. We used to manage the AI pipeline and Pytorch distributed fine-tuning. It took about 3.5 hours to complete on a 4 VM Tanzu cluster with Sapphire Rapids Xeon 4 hardware.

Once the model was fine-tuned with financial data we ran 3 chatbots on a single host. Now that the model was fine-tuned we could ask it questions such as “What is IRR?”, “What in NPV?”, “What is the difference between IRR and NPV?” and get correct and detailed answers back from the LLM.

3 Finance Chatbots running on Sapphire Rapids with AMX

We just took an off-the-shelf LLM, fine-tuned it with financial services information in about 3.5 hours, and now we have a chatbot that can answer basic questions about finance and financial terms. No GPUs were used to do any of this.

You may not want to run every ML workload you have on just CPUs, but there are a lot of them that you can run on just CPUs. Workloads will run even faster with GPUs, but you may not want to pay for GPUs for every workload you run if the speed of a CPU is good enough.

vSphere Requirements for using AMX

If you want to try this in your vSphere environment this is what you’ll need:

  • Hardware with Sapphire Rapids CPUs.
  • Guest VMs running Linux kernel 5.16 or later. Kernel 5.19 or later recommended.
  • Guest VMs using HW version 20 (ESXI 8.0u1, vCenter 8.0u1).
  • If you’re running Kubernetes, your worker nodes will also need to run Linux kernel 5.16 or later.


Obviously you need hardware that supports AMX if you want to use AMX. I’m using Intel Sapphire Rapids Xeon4 CPUs. The hosts have motherboards that support DDR5 memory and PCIE5. In my lab I’m currently testing with Dell R760, Dell R660, and Supermicro SYS-421GE-TNRT servers.

Linux Kernel 5.19 or later

Support for AMX was added to the Linux 5.16 kernel, so if you want to use AMX you’ll need to use 5.16 or a later kernel. In my tests for guest VMs I tried Ubuntu 22.04 images with the 5.19 kernel and images using 6.2 kernels, both of which worked fine. Although Ubuntu 22.04 ships with a 5.15 kernel, the 6.2 kernel is available using the hardware enablement (HWE) kernel package that comes with 22.04. The HWE kernel can be installed with apt:

sudo apt update
sudo apt install \
    --install-recommends \

vSphere 8.0u1 and Hardware Version 20

Which capabilities of the underlying hardware are virtualized in vSphere is determined by the hardware version (HW version) of the guest VM. The AMX instructions are virtualized in HW version 20, so if you want to access AMX instructions in vSphere you need to be using HW version 20 on your VMs.

To find out what HW version a VM is using, in vCenter go to the VM, click the Updates tab, and click the CHECK STATUS button.

HW version 20 is supported on ESXI 8.0u1. To run ESXI 8.0u1 you’ll need vCenter 8.0u1. If you’re still running vCenter 7 and you want to try this technology out I suggest that you upgrade to vCenter 8 as soon as you can, then start upgrading ESXI hosts to ESXI 8.

Once you have a Linux VM with a 5.19 kernel (or later) running HW version 20, any AI/ML framework that you run on that VM will have access to the hardware’s AMX instructions. If you run Docker on the VM any AI/ML containers that you run will be running on a the VM’s kernel and will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.

Tanzu Requirements for using AMX

The kernel requirement also applies to Tanzu worker nodes. Whatever kernel is installed on your worker nodes is the kernel that your Kubernetes pods use. To use AMX your Tanzu worker nodes need to be running kernel 5.16 or later.

Tanzu comes with a set of pre-built, automatically-updated node images called Tanzu Kubernetes Releases (TKRs). Each image is an OVA file that deploys a Kubernetes control node or a worker node. A node is just a Linux VM with a specific version of Kubernetes installed on it and a specific Linux kernel.

When installing Tanzu one of the steps is to set up a Content Library where TKRs are stored. The TKRs are automatically downloaded from VMware into the Content Library whenever new TKRs are released.

When you upgrade a Tanzu Kubernetes cluster, say from Kubernetes 1.23 to 1.24, the Tanzu Supervisor Cluster will create a new VM from 1.24 TKR image, wait for it to join the cluster, then it will evacuate, shut down, and delete one of your 1.23 nodes. The Supervisor Cluster repeats this over and over, first replacing your cluster’s control nodes, then replacing the cluster’s worker nodes, until all of the nodes in the cluster are running Kubernetes 1.24.

Note: Kubernetes should only be upgraded from one minor release to the next minor release. If you have a cluster running Kubernetes 1.20 and you want to upgrade to 1.24, you have to first upgrade to 1.21, then 1.22, then 1.23, and finally to 1.24. Skipping a minor version is not recommended and may break your cluster.

VMware publishes two different TKR images for each version of Kubernetes, one based on PhotonOS and one based on Ubuntu.

At this time VMware has not yet published a TKR with a 5.19 (or later) kernel. If you want to start using Sapphire Rapids AMX instructions and you want to use Tanzu Kubernetes, you have two choices:

  • Wait for the official TKR from VMware with a 5.19 (or later) kernel.
  • Build your own TKR using the Bring Your Own Image (BYOI) process.

Bring Your Own Image (BYOI)

To build an image, follow the instructions on the Github page vSphere Tanzu Kubernetes Grid Image Builder. The process is fairly straightforward. The steps I followed were:

I cloned the repo with git clone:

$ git clone

I edited the packer-variables/vsphere.j2 file so it contained information about my vSphere environment. I also created a folder called “BYOI” under my cluster in vCenter and specified that folder in the config, so any “work in progress” images or VMs generated by the BYOI tool would be created in one place.

Make sure you put the correct values for your vSphere environment in the packer-variables/vsphere.j2 file. The first time I tried this I was using another group’s environment to build a TKR, I used the wrong network name, and I spent about 2 hours trying to figure out why the image was erroring out.

I ran make list-versions to get a list of the available versions:

$ make list-versions
            Kubernetes Version  |  Supported OS
              v1.24.9+vmware.1  |  [photon-3,ubuntu-2004-efi]
       v1.25.7+vmware.3-fips.1  |  [photon-3,ubuntu-2004-efi]

I am going to use v1.24.9+vmware.1, so I ran this to download the artifacts:

$ make run-artifacts-container KUBERNETES_VERSION=v1.24.9+vmware.1
Using default port for artifacts container 8081
Error: No such container: v1.24.9---vmware.1-artifacts-server
Unable to find image '' locally
v1.24.9_vmware.1-tkg.1: Pulling from tkg/tkg-vsphere-linux-resource-bundle
2731d8df91a4: Pull complete
73c864854baf: Pull complete
08eb7dea6abf: Pull complete
52654f918c81: Pull complete
da27b4bff06e: Pull complete
797512e2c717: Pull complete
0a994466e4a6: Pull complete
31d1a74dbc07: Pull complete
b3444fea81b1: Pull complete
193c65bff1b1: Pull complete
Digest: sha256:9dcec246657fa7cf5ece1feab6164e200c9bc82b359471bbdec197d028b8e577
Status: Downloaded newer image for

Customize the TKR OVA Image

The last step is to build the TKR OVA file, but before I build it I want to add two customizations. I need to need to use VM Hardware version (aka “VMX version”) 20 for the OVA, and I need to make sure that we build an Ubuntu OVA with a kernel >= 5.16.

The Github README docs have examples of how to customize the OVA. The first example shows how to change the HW version, and the second one shows how to add new OS packages. Reading those two examples tells me what I need to do.

Use HW Version 20 for the Image

I edit the packer-variables/default-args.j2 file and change the vmx_version:

    "vmx_version": "20",

Install a Kernel >= 5.16 on the Image

Earlier when I ran make list-versions I noticed that the v1.24.9+vmware.1 Kubernetes version supports Ubuntu 20.04. However, the only way to get a packaged kernel >= 5.16 installed is to install the Ubuntu 22.04 linux-image-generic-hwe-22.04 package, and vsphere-tanzu-kubernetes-grid-image-builder does not currently have a base image for 22.04.

Since I need 22.04, and 20.04 is the only version available, I’m going to force Packer to do a release upgrade before generating the OVA. To do that I’m going to install the jammy-updates repo from 22.04. When I do that, the vSphere Tanzu Kubernetes Grid Image Builder will cause Packer to upgrade the image to Ubuntu 22.04 and I can then install the Ubuntu 22.04 linux-image-generic-hwe-22.04 package.

Following the instructions from Adding new OS packages and configuring the repositories or sources:

I create a directory repos under ansible/files/

I create a file ansible/files/repos/ubuntu.list which contains the lines:

deb jammy-updates main restricted
deb jammy-security main restricted
deb jammy main restricted

I create the file packer-variables/repos.j2 which contains:

    {% if os_type == "photon-3" %}
    "extra_repos": "/image-builder/images/capi/image/ansible/files/repos/photon.repo"
    {% elif os_type == "ubuntu-2004-efi" %}
    "extra_repos": "/image-builder/images/capi/image/ansible/files/repos/ubuntu.list"
    {% endif %}

Doing all of that will add the jammy-updates repo to the TKR image. Now to add the kernel package I go back to the same packer-variables/default-args.j2 file we were editing earlier, I look for the extra_debs line and add the HWE kernel package for Ubuntu 22.04, linux-image-generic-hwe-22.04:

"extra_debs": "unzip iptables-persistent nfs-common linux-image-generic-hwe-22.04",

Now that I’ve made those changes I can build the TKR OVA.

Build the Image

The main Github README page says I can run make build-node-image to build the OVA, but I want to use a specific version of Kubernetes and I want to use Ubuntu 20.04, so I assume I need to pass some extra parameters to make. Typing make help gives me all of the information I need to construct the right build command:

IP=[my VM's IP address, where the artifact container is running]
make build-node-image \
    OS_TARGET=ubuntu-2004-efi \
    KUBERNETES_VERSION=v1.24.9+vmware.1 \
    TKR_SUFFIX=spr \
    HOST_IP=$IP \

This takes a while to run and will create and configure a VM on your vSphere cluster that will be used to create the TKR OVA image. If you want to watch the build, run the docker logs command that make build-node-image spits out:

docker logs -f v1.24.9---vmware.1-ubuntu-2004-efi-image-builder

When the process is done you should have an image file named ${HOME}/image/ovas/ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova

Add the Image to a local Content Library

In order for Tanzu to be able to use the image it has to be added to a local content library. If you don’t have a local content library create one by going to vSphere Client > Content Libraries > Create.

Once you’ve created the library click the library name to pull it up on the screen and click Actions > Import Item. Upload the ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova file.

Associate the Content Library with the Cluster Namespace

Go to vSphere Client > Workload Management > “your cluster namespace”, then click MANAGE CONTENT LIBRARIES on the VM Service tile. Make sure that the local library, and any other libraries used by your Cluster Namespace, are checked.

Deploy Your Own Image

To create a Kubernetes cluster you create a YAML file and run kubectl on in. The following YAML file builds a cluster based on the ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova TKR image, which is based on Ubuntu 20.04 and contains Kubernetes 1.24.9 and a Linux HWE kernel (currently kernel 6.2).

kind: TanzuKubernetesCluster
  name: my-tanzu-kubernetes-cluster-name
  namespace: my-tanzu-kubernetes-cluster-namespace
  annotations: os-name=ubuntu
      replicas: 3
      vmClass: guaranteed-small
      storageClass: vsan-default-storage-policy
          name: v1.24.9---vmware.1-spr
    - name: worker
      replicas: 3
      vmClass: guaranteed-8xlarge
      storageClass: vsan-default-storage-policy
        - name: containerd
          mountPath: /var/lib/containerd
            storage: 160Gi
          name: v1.24.9---vmware.1-spr

A couple of notes on this YAML file:

  • For a stable, easily-upgradable cluster I recommend a minimum of 3 control plane nodes and 3 worker nodes.
  • The metadata section’s annotations line must be present to use an Ubuntu TKR as the base image.
  • The TKR reference just refers to the first part of the TKR’s file name. You can see the TKR file names by looking in the vCenter Content Library you set up for Tanzu. To get a list of valid reference names:
    kubectl config use-context $my-tanzu-kubernetes-cluster-namespace
    kubectl get tanzukubernetesreleases

    Only the names that have READY=True and COMPATIBLE=True can be used to deploy a cluster.
  • In order to allocate a separate, larger volume for storing docker images on the worker nodes I added a volumes section. I have a storage class defined named vsan-default-storage-policy and the volumes section will allocate a 160GiB volume using the disk specified by vsan-default-storage-policy and mount it on the worker node using the path /var/lib/containerd, which is where container images are stored. Change vsan-default-storage-policy to the name of a storage policy defined for your tanzu-kubernetes-cluster-namespace if you want this to work on your system.
  • Since images are downloaded as needed, the containerd volume will be destroyed when a worker node is destroyed. It will be destroyed and recreated (empty) when a worker node is upgraded.

I recommend deploying a fresh cluster using this YAML file just so you can try it out and see how it works. Once you’ve deployed a new cluster any AI/ML containers that you run will be running on a 6.2 kernel and will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.

Upgrading an existing Tanzu Kubernetes cluster to the new TKR image

To upgrade an existing Tanzu Kubernetes 1.23 cluster to 1.24 using the new TKR image:

  • Modify the existing 1.23 cluster’s YAML file to refer to the v1.24.9---vmware.1-spr TKR image.
  • Make sure that the YAML file has the annotations line so the Supervisor will deploy an Ubuntu-based TKR.

Then run:

kubectl config use-context $my-tanzu-kubernetes-cluster-namespace
kubectl apply -f $my-yaml-filename

If you can’t find your cluster’s YAML file you can also do this:

kubectl config use-context $my-tanzu-kubernetes-cluster-namespace
kubectl edit tanzukubernetescluster/$my-tanzu-kubernetes-cluster-name

This will pull up a system editor (vim on my system) containing the cluster’s freshly-generated current YAML file. Make the changes and save the file. Any changes you make will be applied immediately when you save the file.

Check the deployed cluster VMs

You can ssh into a cluster’s VMs and check the kernel version running and verify that you can see the amx and avx flags for the CPUs, indicating that the extra instructions are accessible. In vCenter find one of the cluster’s VMs and get the IP address. To get the ssh password:

kubectl config use-context my-tanzu-kubernetes-cluster-namespace
kubectl get secret \
    my-tanzu-kubernetes-cluster-name-ssh-password \
    -o jsonpath='{.data.ssh-passwordkey}' \
    -n my-tanzu-kubernetes-cluster-namespace | base64 -d
ssh -o PubkeyAuthentication=no vmware-system-user@vm-ip-address

$ uname -a
Linux my-tanzu-kubernetes-cluster-name-02-twk2c-wzsjc 6.2.0-26-generic #26~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jul 13 16:27:29 UTC 2 x86_64 x86_64 x86_64 GNU/Linux

$ grep VERSION_ID /etc/os-release

$ grep avx /proc/cpuinfo | head -1
flags		: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities

With these instructions you should now be able to create VMs and Kubernetes clusters that can access Sapphire Rapids AMX instructions. Any AI/ML framework that you run will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.

Hope you find this useful.


Run a Linux systemd service during shutdown

I recently needed to add a cleanup service that runs at shutdown to a hundred AWS servers. My requirements were:

  • Run the script /usr/local/sbin/ when a VM shuts down (poweroff or reboot).
  • Send the output from the script to the syslog service.

So I needed to create a systemd service file that would call the script when the VM shuts down. This is the ec2-cleanup.service file I created:

# ec2-cleanup.service

Description=Run cleanup at shutdown



Type=oneshot means that the command runs once. Normally since this is a oneshot service the service would exit after the ExecStart command runs, but since I don’t want to do anything when the service starts, there is is no ExecStart command. That’s why I use RemainAfterExit=yes, which keeps the service running even though there’s no ExecStart command.

Finally I use ExecStop to run the command at shutdown time.

After=syslog.service ensures that the ec2-cleanup.service doesn’t start until after syslog service is running and the network has started. More importantly, since systemd stops services in the reverse order that they’re started, this also ensures that syslog and the network service are still running when systemd runs the ec2-cleanup.service‘s ExecStop command.

Although there are many different available syslog services, most use “syslog” as a service alias, so After=syslog.service should work regardless of which syslog service you actually use. (e.g. If you use rsyslog this still works, because rsyslog declares syslog as an alias.)

Finally, I just needed to install the service on my AWS VMs, so I added this to an Ansible playbook that runs on my AWS VMs:

  - name: Install the script
      dest: /usr/local/sbin/
      owner: root
      group: root
      mode: 0755

  - name: Install a service to run at shutdown
      src: ec2-cleanup.service
      dest: /lib/systemd/system/ec2-cleanup.service
      owner: root
      group: root
      mode: 0644
    register: ec2_cleanup_service

  - name: Restart ec2-cleanup service if the service file changed
      name: ec2-cleanup
      daemon_reload: True
      state: restarted
    when: ec2_cleanup_service.changed

  - name: Enable ec2-cleanup service so it starts on boot
      name: ec2-cleanup
      enabled: True
      state: started

To verify that all of this works I ran the Ansible playbook on a VM, then logged in and checked the status of the service:

eruby@i-056ac231adeb1f930:~$ systemctl status ec2-cleanup
● ec2-cleanup.service - Run cleanup at shutdown
     Loaded: loaded (/lib/systemd/system/ec2-cleanup.service; enabled; vendor preset: enabled)
     Active: active (exited) since Tue 2023-03-14 17:04:37 UTC; 44s ago

Mar 14 17:04:37 i-056ac221aceb1f830 systemd[1]: Finished Run cleanup at shutdown.

The service is active (exited), which I expected (exited because ExecStart has completed, active because RemainAfterExit=yes is keeping the service running until shutdown.

If I reboot the VM and log back in I can check syslog with:

journalctl -u ec2-cleanup.service -n 20

… and see the last 20 lines of output from the script. The log output shows that the script ran when I rebooted.

Hope you find this useful.


Quickly create guest VMs using virsh, cloud image files, and cloud-init

After the latest updates to the code these scripts now create VMs from full Linux distros in a few seconds.

I was looking for a way to automate the creation of VMs for testing various distributed system / cluster software packages. I’ve used Vagrant in the past but I wanted something that would:

  • Allow me to use raw image files as the basis for guest VMs.
  • Guest VMs should be set up with bridged IPs that are routable from the host.
  • Guest VMs should be able to reach the Internet.
  • Other hosts on the local network should be able to reach guest VMs. (Setting up additional routes is OK).
  • VM creation should work with any distro that supports cloud-init.
  • Scripts should be able to create and delete VMs in a scripted, fully-automatic manner.
  • Guest VMs should be set up to allow passwordless ssh access from the “ansible” user, so that once a VM is running Ansible can be used for additional configuration and customization of the VM.

I’ve previously used virsh’s virt-install tool to create VMs and I like how easy it is to set up things like extra network interfaces and attach existing disk images. The scripts in this repo fully automate the virsh VM creation process.


The current version of the create-vm script uses cloud images, cloud-init, and virsh tooling to quickly create VMs from the command line. Using a single Linux host system you can create multiple guest VMs running on that host. Each guest VM has its own file system, memory, virtualized CPUs, IP address, etc.

Cloud Images

create-vm creates a QCOW2 file for your VM’s file system. The QCOW2 image uses the cloud image as a base filesystem, so instead of copying all of the files that come with a Linux distribution and installing them, QCOW will just use files directly from the base image as long as those files remain unchanged. QCOW stands for “QEMU Copy On Write”, so once you make a change to a file the changes are written to your VM’s QCOW2 file.

Cloud images have the extension .img or .qcow and are compiled for different system architectures.

Cloud images are available for the following distros:

Pick the base image for the distro and release that you want to install and download it onto your host system. Make sure that the base image uses the same hardware architecture as your host system, e.g. “x86_64” or “amd64” for Intel and AMD -based host systems, “arm64” for 64 bit ARM-based host systems.

cloud-init configuration

cloud-init reads in two configuration files, user-data and meta-data, to initialize a VM’s settings. One of the places it looks for these files is any attached disk volume labeled cidata.

The create-vm script creates an ISO disk called cidata with these two files and passes that in as a volume to virsh when it creates the VM. This is referred to as the “no cloud” method, so if you see a cloud image for “nocloud” that’s the one you want to use.

If you’re interested in other ways of doing this check out the Datasources documentation on for cloud-init.


create-vm stores files as follows:

  • ${HOME}/vms/base/ – Place to store your base Linux cloud images.
  • ${HOME}/vms/images/ – your-vm-name.img and your-vm-name-cidata.img files.
  • ${HOME}/vms/init/ – user-data and meta-data.
  • ${HOME}/vms/xml/ – Backup copies of your VMs’ XML definition files.

QCOW2 filesystems allocate space as needed, so if you create a VM with 100GB of storage, the initial size of the your-vm-name.img and your-vm-name-cidata.img files is only about 700K total. The your-vm-name.img file will grow as you install packages and update files, but will never grow beyond the disk size that you set when you create the VM.


The create-vm repo contains these scripts:

  • create-vm – Use .img and cloud-init files to auto-generate a VM.
  • delete-vm – Delete a virtual machine created with create-vm.
  • get-vm-ip – Get the IP address of a VM managed by virsh.

Host setup

I’m running the scripts from a host with Ubuntu Linux 22.04 installed. I added the following to the host’s Ansible playbook to install the necessary virtualization packages:

  - name: Install virtualization packages
      name: "{{item}}"
      state: latest
    - libvirt-bin
    - libvirt-clients
    - libvirt-daemon
    - libvirt-daemon-system
    - libvirt-daemon-driver-storage-zfs
    - python-libvirt
    - python3-libvirt
    - virt-manager
    - virtinst

If you’re not using Ansible just apt-get install the above packages.


The libvirtd daemon runs under the libvirt-qemu user service account. The libvirt-qemu user must be able to read the files in ${HOME}/vms/. If your ${HOME} directory has permissions set to 0x750 then libvirt-qemu won’t be able to read the ${HOME}/vms/ directory.

You could open up your home directory, e.g.:

chmod 755 ${HOME}

… but that allows anyone logged into your Linux host to read everything in your home directory. A better approach is just to add libvirt-qemu to your home directory’s group. For instance, on my host my home directory is /home/earl owned by user earl and group earl, permissions 0x750:

$ chmod 750 /home/earl
$ ls -al /home
total 24
drwxr-xr-x   6 root      root      4096 Aug 28 21:26 .
drwxr-xr-x  21 root      root      4096 Aug 28 21:01 ..
drwxr-x--- 142 earl      earl      4096 Feb 16 09:27 earl

To make sure that only the libvirt-qemu user can read my files I can add the user to the earl group:

$ sudo usermod --append --groups earl libvirt-qemu
$ sudo systemctl restart libvirtd
$ grep libvirt-qemu /etc/group

That shows that the group earl, group ID 1000, has a member libvirt-qemu. Since the group earl has read and execute permissions on my home directory, libvirt-qemu has read and execute permissions on my home directory.

Note: The libvirtd daemon will chown some of the files in the directory, including the files in the ~/vms/images directory, to be owned by libvirt-qemu group kvm. In order to delete these files without sudo, add yourself to the kvm group, e.g.:

$ sudo usermod --append --groups kvm earl

You’ll need to log out and log in again before the additional group is active.

create-vm options

create-vm supports the following options:

   -h      Show this message
   -n      Host name (required)
   -i      Full path and name of the base .img file to use (required)
   -k      Full path and name of the ansible user's public key file (required)
   -r      RAM in MB (defaults to 2048)
   -c      Number of VCPUs (defaults to 2)
   -s      Amount of storage to allocate in GB (defaults to 80)
   -b      Bridge interface to use (defaults to virbr0)
   -m      MAC address to use (default is to use a randomly-generated MAC)
   -v      Verbose

Create an Ubuntu 22.04 server VM

This creates an Ubuntu 22.04 “Jammy Jellyfish” VM with a 40G hard drive.

First download a copy of the Ubuntu 22.04 “Jammy Jellyfish” cloud image:

mkdir -p ~/vms/base
cd ~/vms/base

Then create the VM:

create-vm -n node1 \
    -i ~/vms/base/jammy-server-cloudimg-amd64.img \
    -k ~/.ssh/ \
    -s 40

Once created I can get the IP address and ssh to the VM as the user “ansible”:

$ get-vm-ip node1
$ ssh -i ~/.ssh/id_rsa_ansible ansible@
The authenticity of host ' (' can't be established.
ED25519 key fingerprint is SHA256:L88LPO9iDCGbowuPucV5Lt7Yf+9kKelMzhfWaNlRDxk.
This key is not known by any other names
Are you sure you want to continue connecting (yes/no/[fingerprint])? yes
Warning: Permanently added '' (ED25519) to the list of known hosts.
Welcome to Ubuntu 22.04.1 LTS (GNU/Linux 5.15.0-60-generic x86_64)

 * Documentation:
 * Management:
 * Support:

  System information as of Wed Feb 15 20:05:45 UTC 2023

  System load:  0.47216796875     Processes:             105
  Usage of /:   3.7% of 38.58GB   Users logged in:       0
  Memory usage: 9%                IPv4 address for ens3:
  Swap usage:   0%

Expanded Security Maintenance for Applications is not enabled.

0 updates can be applied immediately.

Enable ESM Apps to receive additional future security updates.
See or run: sudo pro status

The programs included with the Ubuntu system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.

Ubuntu comes with ABSOLUTELY NO WARRANTY, to the extent permitted by
applicable law.

To run a command as administrator (user "root"), use "sudo <command>".
See "man sudo_root" for details.

ansible@node1:~$ df -h
Filesystem      Size  Used Avail Use% Mounted on
tmpfs           198M 1008K  197M   1% /run
/dev/sda1        39G  1.5G   38G   4% /
tmpfs           988M     0  988M   0% /dev/shm
tmpfs           5.0M     0  5.0M   0% /run/lock
/dev/sda15      105M  6.1M   99M   6% /boot/efi
tmpfs           198M  4.0K  198M   1% /run/user/1000

Note that this VM was created with a 40GB hard disk, and the total disk space shown is 40GB, but the actual hard drive space initially used by this VM was about 700K. The VM can consume up to 40GB, but will only use the space it actually needs.

Create 8 Ubuntu 22.04 servers

This starts the VM creation process and exits. Creation of the VMs continues in the background.

for n in `seq 1 8`; do
    create-vm -n node$n -i ~/vms/base/jammy-server-cloudimg-amd64.img -k ~/.ssh/

Delete 8 virtual machines

for n in `seq 1 8`; do
    delete-vm node$n

Connect to a VM via the console

virsh console node1

Connect to a VM via ssh

ssh ansible@$(get-vm-ip node1)

Generate an Ansible hosts file

    echo '[hosts]'
    for n in `seq 1 8`; do
        ip=$(get-vm-ip node$n)
        echo "node$n ansible_host=$ip ip=$ip ansible_user=ansible"
) > hosts.ini

Handy virsh commands

virsh list – List all running VMs.

virsh domifaddr node1 – Get a node’s IP address. Does not work with all network setups, which is why I wrote the get-vm-ip script.

virsh net-list – Show what networks were created by virsh.

virsh net-dhcp-leases $network – Shows current DHCP leases when virsh is acting as the DHCP server. Leases may be shown for machines that no longer exist.

Hope you find this useful.


Setting up a 100GbE PVRDMA Network on vCenter 7

After writing my last article on Getting NVIDIA NGC containers to work with VMware PVRDMA networks I had a couple of people ask me “How do I set up PVRDMA networking on vCenter?” These are the steps that I took to set up PVRDMA networking in my lab.

RDMA over Converged Ethernet (RoCE) is a network protocol that allows remote direct memory access (RDMA) over an Ethernet network. It works by encapsulating an Infiniband (IB) transport packet and sending it over Ethernet. If you’re working with network applications that require high bandwidth and low latency, RDMA will give you lower latency, higher bandwidth, and a lower CPU load than an API such as Berkeley sockets.

Full disclosure: I used to work for a startup called Bitfusion, and that startup was bought by VMware, so I now work for VMware. At Bitfusion we developed a technology for accessing hardware accelerators, such as NVIDIA GPUs, remotely across networks using TCP/IP, Infiniband, and PVRDMA. I still work on the Bitfusion product at VMware, and spend a lot of my time getting AI and ML workloads to work across networks on virtualized GPUs.

In my lab I’m using Mellanox Connect/X5 and ConnectX/6 cards on hosts that are running ESXi 7.0.2 and vCenter 7.0.2. The cards are connected to a Mellanox Onyx MSN2700 100GbE switch.

Since I’m working with Ubuntu 18.04 and 20.04 virtual machines (VMs) in a vCenter environment, I have a couple of options for high-speed networking:

  • I can use PCI passthrough to pass the PCI network card directly through to the VM and use the network card’s native drivers on the VM to set up a networking stack. However this means that my network card is only available to a single VM on the host, and can’t be shared between VMs. It also breaks vMotion (the ability to live-migrate the VM to another host) since the VM is tied to a specific piece of hardware on a specific host. I’ve set this up in my lab but stopped doing this because of the lack of flexibility and because we couldn’t identify any performance difference compared to SR-IOV networking.
  • I can use SR-IOV and Network Virtual Functions (NVFs) to make the single card appear as if it’s multiple network cards with multiple PCI addresses, pass those through to the VM, and use the network card’s native drivers on the VM to set up a networking stack. I’ve set this up in my lab as well. I can share a single card between multiple VMs and the performance is similar to PCI passthough. The disadvantages are that setting up SR-IOV and configuring the NVFs is specific to a card’s model and manufacturer, so what works in my lab might not work in someone else’s environment.
  • I can set up PVRDMA networking and use the PVRDMA driver that comes with Ubuntu. This is what I’m going to show how to do in this article.

Set up your physical switch

First, make sure that your switch is set up correctly. On my Mellanox Onyx MSN2700 100GbE switch that means:

  • Enable the ports you’re connecting to.
  • Set the speed of each port to 100G.
  • Set auto-negotiation for each link.
  • MTU: 9000
  • Flowcontrol Mode: Global
  • LAG/MLAG: No
  • LAG Mode: On

Set up your virtual switch

vCenter supports Paravirtual RDMA (PVRDMA) networking using Distributed Virtual Switches (DVS). This means you’re setting up a virtual switch in vCenter and you’ll connect your VMs to this virtual switch.

In vCenter navigate to Hosts and Clusters, then click the DataCenter icon (looks like a sphere or globe with a line under it). Find the cluster you want to add the virtual switch to, right click on the cluster and select Distributed Switch > New Distributed Switch.

  • Name: “rdma-dvs”
  • Version: 7.0.2 – ESXi 7.0.2 and later
  • Number of uplinks: 4
  • Network I/O control: Disabled
  • Default port group: Create
  • Port Group Name: “VM 100GbE Network”

Figure out which NIC is the right NIC

  • Go to Hosts and Clusters
  • Select the host
  • Click the Configure tab, then Networking > Physical adapters
  • Note which NIC is the 100GbE NIC for each host

Add Hosts to the Distributed Virtual Switch

  • Go to Hosts and Clusters
  • Click the DataCenter icon
  • Select the Networks top tab and the Distributed Switches sub-tab
  • Right click “rdma-dvs”
  • Click “Add and Manage Hosts”
  • Select “Add Hosts”
  • Select the hosts. Use “auto” for uplinks.
  • Select the physical adapters based on the list you created in the previous step, or find the Mellanox card in the list and add it. If more than one is listed, look for the card that’s “connected”.
  • Manage VMkernel adapters (accept defaults)
  • Migrate virtual machine networking (none)

Tag a vmknic for PVRDMA

  • Select an ESXi host and go to the Configure tab
  • Go to System > Advanced System Settings
  • Click Edit
  • Filter on “PVRDMA”
  • Set Net.PVRDMAVmknic = "vmk0"

Repeat for each ESXi host.

Set up the firewall for PVRDMA

  • Select an ESXi host and go to the Configure tab
  • Go to System > Firewall
  • Click Edit
  • Scroll down to find pvrdma and check the box to allow PVRDMA traffic through the firewall.

Repeat for each ESXi host.

Set up Jumbo Frames for PVRDMA

To enable jumbo frames a vCenter cluster using virtual switches you have to set MTU 9000 on the Distributed Virtual Switch.

  • Click the Data Center icon.
  • Click the Distributed Virtual Switch that you want to set up, “rdma-dvs” in this example.
  • Go to the Configure tab.
  • Select Settings > Properties.
  • Look at Properties > Advanced > MTU. This should be set to 9000. If it’s not, click Edit.
  • Click Advanced.
  • Set MTU to 9000.
  • Click OK.

Add a PVRDMA NIC to a VM

  • Edit the VM settings
  • Add a new device
  • Select “Network Adapter”
  • Pick “VM 100GbE Network” for the network.
  • Connect at Power On (checked)
  • Adapter type PVRDMA (very important!)
  • Device Protocol: RoCE v2

Configure the VM

For Ubuntu:

sudo apt-get install rdma-core infiniband-diags ibverbs-utils

Tweak the module load order

In order for RDMA to work the vmw_pvrdma module has to be loaded after several other modules. Maybe someone else knows a better way to do this, but the method that I got to work was adding a script /usr/local/sbin/ to ensure that Infiniband modules are loaded on boot, then calling that from /etc/rc.local so it gets executed at boot time.

# modules that need to be loaded for PVRDMA to work
/sbin/modprobe mlx4_ib
/sbin/modprobe ib_umad
/sbin/modprobe rdma_cm
/sbin/modprobe rdma_ucm

# Once those are loaded, reload the vmw_pvrdma module
/sbin/modprobe -r vmw_pvrdma
/sbin/modprobe vmw_pvrdma

Once that’s done just set up the PVRDMA network interface the same as any other network interface.

Testing the network

To verify that I’m getting something close to 100Gbps on the network I use the perftest package.

To test bandwith I pick two VMs on different hosts. On one VM I run:

$ ib_send_bw --report_gbits

On the other VM I run the same command plus I add the IP address of the PVRDMA interface on the first machine:

$ ib_send_bw --report_gbits

That sends a bunch of data across the network and reports back:

So I’m getting an average of 96.31Gbps over the network connection.

I can also check the latency using the ib_send_lat:

Hope you find this useful.


Mouse button Copy & Paste on Ubuntu 20.04

Using the left mouse button to select and copy text in terminals and the middle mouse button to paste has been a feature of X-Windows, and the various window managers built on top of X-Windows, since the early 1990s. With the release of Ubuntu 20.04 and Gnome 3.36 Canonical has removed this convention, forcing a more awkward and slower select, right click, select Copy from a menu, point, right click, select Paste from menu to do the same thing.

If you want to restore select-to-copy, middle button to paste functionality to Ubuntu 20.04 just follow these steps.

Restore select-to-copy functionality

Edit the file .Xresources in your home directory.

Add the line:

xterm*selectToClipboard: true

… to the file, then logout of your desktop and log back in, or reboot.

Once you’ve done that any text that you select in the Terminal program with your left mouse button will be copied to your clipboard. Left click a word and the word is copied to the clipboard. Left click and drag to select and copy an entire line, an entire paragraph, or more.

Restore middle-button paste functionality

Install gnome-tweaks:

sudo apt-get install gnome-tweaks

Click “Activities” in the upper right and search for “tweaks”, click the “Tweaks” icon.

Select “Keyboard & Mouse” and turn “Middle Click Paste” to “on”.

Once you’ve done that, clicking the middle mouse button will paste text from your clipboard back into the terminal.

Hope you find this useful.