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Quickly deploying vSphere IaaS (formerly vSphere with Tanzu) Control Plane Services via YAMLs

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I recently deployed the Local Consumption Interface (LCI) which is a new vSphere IaaS (formerly vSphere with Tanzu) Control Plane Service that was just introduced with the release of vSphere 8.0 Update 3 into my homelab.

While going through the manual vSphere UI deployment, I noticed there were only handful of vSphere IaaS services that are shipped out of the box including the VM Service (VM Operator), Velero vSphere Operator and Tanzu Kubernetes Grid (TKG) Service.


The workflow to register a new vSphere IaaS service is not difficult, but it does take multiple steps from downloading the deployment YAML file, uploading the file to vCenter Server and then confirm the deployment before the deployment of the service begins and this would have to be done for each vSphere IaaS service you wish to deploy!

There has to be a better and more streamline way, especially for those that do not like to click to many times in the UI 😅

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Identifying vSphere with Tanzu Managed VMs

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With an increasing number of customers enabling the vSphere with Tanzu capability, which is included in both VMware vSphere Foundation (VVF) and VMware Cloud Foundation (VCF), more and more Virtual Machines will be deployed using the more modern approach of declarative provisioning using the powerful VM Service feature.


When using the vSphere UI, you can easily distinguish between a vSphere with Tanzu managed VM from a traditional VM by checking whether it is provisioned within a vSphere Namespace or whether it contains the "Developer Managed" label as shown in the screenshot above.

However, how do you identify a vSphere with Tanzu managed VM when using the vSphere API for Automation purposes?

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NVIDIA GPU with Dynamic DirectPath IO (Passthrough) to Tanzu Kubernetes Grid (TKG) Cluster using vSphere with Tanzu

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When provisioning a Tanzu Kubernetes Grid Cluster (TKC) using vSphere with Tanzu, you can easily request an NVIDIA GPU resource as part of the deployment, which can either be provided by NVIDIA vGPU or using PCIe passthrough with Dynamic DirectPath IO.

vGPU is great for those with a capable NVIDIA GPU, especially if the GPU will not be utilized 100% and you can share its resources amongst several VMs. However, if you do not have a capable GPU that supports vGPU, you can still provide you TKC workloads with a GPU resource using passthrough.


While playing with the Lenovo P3 Ultra, I unfortunately came to learn that NVIDIA RTX A5500 Laptop was NOT the same as an NVIDIA RTX A5500 🙁

Not ideal, but I guess NVIDIA did not want to add this additional device to their test matrix and hence their ESXi graphics drivers would not detect the GPU as vGPU capable. I knew that I could still use the NVIDIA GPU via passthrough but to my surprise, I just needed to get the NVIDIA drivers installed onto the TKC worker nodes.

That was much easier said than done as all the documentation that I could find on both VMware and NVIDIA website had detailed instructions for vGPU configuration but there was little to no documentation on how to use NVIDIA GPU in passthrough mode with vSphere with Tanzu. I came across a number of different NVIDIA solutions when it comes to k8s, but it was not very clear on which would be interoperable with vSphere with Tanzu and I eventually figured it out with the help pointing me in the right direction.

It was actually super easy, once you knew the exact steps! 😅

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