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ESXi support for Intel iGPU with SR-IOV

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Support for Single Root I/O Virtualization (SR-IOV) was first introduced back in 2012 with the release of vSphere 5.1 and enables for a physical PCIe device to be shared amongst a number of Virtual Machines. The networking industry was the first to take advantage of the SR-IOV technology and could be used to help reduce latencies and improve overall CPU efficiencies for vSphere-based workloads that were network intensive.

Since SR-IOV is an extension of the PCIe specification, it can also be used benefit other non-networking devices. In 2016, AMD introduced their MxGPU technology which added SR-IOV capabilities to their GPUs which was then used to power VMware Horizon workloads, but this functionality was only available during the vSphere 6.0 and 6.5 release.

GPU sharing these days are synonymous to one vendor, NVIDIA. In 2015, VMware and NVIDIA teamed up to accelerate Enterprise desktop workloads through the integration of NVIDIA's vGPU (formally GRID) technology with the release of both VMware Horizon View and vSphere 6.0.

NVIDIA continues to dominate the GPU market in 2023, however another vendor has re-entered the market with an interesting solution that is enabled by the latest vSphere 8.0 Update 2 release ...

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New detailed GuestOS data in vSphere 8.0 Update 2

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Your workloads are constantly changing both from an application standpoint but also the underlying guest operating system (GuestOS) including patches and upgrades. While many organizations have a change management database (CMDB), it can still be a challenge to true up that information from when the workload was first deployed to what it is currently running.

Back in 2019, VMware enhanced VMware Tools to make it easier to identify applications and processes that was running within a VM and this also provided a programmatic way for retrieving this inventory information which could then be fed back into their CMDB.

Applications are not the only things that change inside of a VM, the GuestOS probably receives the most updates (patches/upgrades) and being able to accurately inventory this information without relying on an in-guest agent is still a challenge for many.

In vSphere 8.0 Update 2, we have enhanced the available GuestOS data using VMware Tools and we have also made it easier to inventory and collect this information even when the VM has been powered off.

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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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