VMWare - Redhat 9 Install NVidia Pass through and Container Setup

Installing an NVIDIA driver in a Red Hat 9 virtual machine (VM) with GPU passthrough involves several steps, including configuring both the VMware ESXi host and the RHEL guest OS. GPU passthrough dedicates the entire physical GPU to a single VM, bypassing the hypervisor. 

Reviewed 7October Oct2026 2026. Current releases:against RHEL 9.8 (May 2026) and NVIDIA data center driver branch 615. This VM'sguide GPUuses isan aNVIDIA Tesla M10 (Maxwell). NVIDIA driver branch R580 is the last branch that supports Maxwell, Pascal and Volta.Volta GPUs. It is a Long Term Support Branch with end of life in June 2028, so thisa VMMaxwell card must stay on the 580 proprietary DKMS stream. Newer branches drop Maxwell, and from branch 615 NVIDIA ships only the open kernel modules, which need Turing or newer.

Step 1: Enable GPU passthrough on the ESXi host

1. In the host BIOS/UEFI, make sure Intel VT-d (AMD: IOMMU) is enabled and PCI mapping above 4 GB is allowed.
2. In the vSphere Client select the host, then Configure > Hardware > PCI Devices and open All PCI Devices. (ESXi Host Client: Manage > Hardware > PCI Devices.)
3. FindSelect yourthe NVIDIA GPU device in the list and click Toggle Passthrough.
4. If the device shows an orange icon, put the host mustin be rebooted (maintenance mode first)and reboot it before using the device can be used.device. The passthrough status should then show as Enabled/Active.

Step 2: Configure the RHEL 9 virtual machine

1. Power off the virtual machine. This is criticalrequired before assigning PCI devicesdevices..
2. Edit the settings of yourthe RHEL 9 VM in the vSphere Client.
3. Under VM Options > Boot Options set Firmware to EFI., because GPU passthrough VMs will not boot correctly on legacy BIOS. Make sure Secure Boot is disabled; see Step 4 forexplains the reason.why.
4. Under Virtual Hardware,
   1. Clickclick Add New Device and, choose PCI Device.
   2. From the dropdown menu,and select the NVIDIA GPU you just configuredenabled for passthrough.
5. Under Memory, reserve all guest memory (All locked). A passthrough device needs the full memory reservation.
6. NavigateGo to VM Options > Advanced and, click Edit Configuration.
7. Addand oradd modifythese theparameters: following key-value pairs:
   • pciPassthru.use64bitMMIO = TRUE
   • pciPassthru.64bitMMIOSizeGB = 64: the value depends on your card'sGPU memory. Broadcom's rule is to add up the framebuffer memory of every GPU in the VM. If the total is a power of 2, use the next power of 2. If it falls between two powers of 2, round up twice. One Tesla M10 GPU (8 GB) needs at least 16; 64 also works.
   • hypervisor.cpuid.v0 = FALSE: keptused fromin the originaltested working build.setup. It is not part of Broadcom's GPU passthrough KBs.
8. Save the VM settings and power on the VM.

Step 3: Prepare the OS

VMware tools: Broadcom recommends the open-vm-tools package shipped by Red Hat.

sudo dnf install -y open-vm-tools

Enable CodeReady Builder and EPEL: the NVIDIA driver RPMs depend on packages that only exist in EPEL (for example DKMS).

sudo subscription-manager repos --enable=codeready-builder-for-rhel-9-x86_64-rpms
sudo dnf install https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm

Install build dependencies: youDKMS needneeds the kernel headers and development packages for the DKMS build.packages. On RHEL 9 NVIDIA uses kernel-devel-matched, which also pullsinstalls the matching devel package for future kernel updates so DKMS can rebuild the module.

sudo dnf groupinstall "Development Tools"
sudo dnf install kernel-devel-matched kernel-headers

DisablingDisable the Nouveau driver:driver (optional): the open-source Nouveau driver can conflict with the NVIDIA driver. The NVIDIA RPMs now disable nouveau automatically during install (nvidia-boot-update),. so this step is optional. If youTo do it by hand, note the option is modeset (the old page had a typo, modset).hand:

sudo bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
sudo bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"

Regenerate the initial RAM disk image and reboot

sudo dracut --force
sudo reboot

Step 4: Install the NVIDIA driver (Tesla M10 = 580 branch, proprietary DKMS)

DoFor Maxwell GPUs such as the Tesla M10, do not use nvidia-driver:latest-dkms, nvidia-driver:open-dkms or nvidia-open on this VM.. In NVIDIA's RHEL 9 reporepository latest-dkms now resolves to the 610 branch, which no longer supports Maxwell. The open kernel modules only support Turing and newer GPUs.

Add the repository: NVIDIA's network repository for RHEL 99.

sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
sudo dnf clean expire-cache

Install the driver: use the dnf module stream locked to the 580 branch with proprietary DKMS modules. Its default profile installs the full driver plus kmod-nvidia-latest-dkms.

sudo dnf module install nvidia-driver:580-dkms

Later updates stay on the 580 branch with a normal sudo dnf update.

Secure Boot: the DKMS-built proprietary modules are not signed by Red Hat. Red Hat-signed precompiled NVIDIA modules exist only for the open kernel modules (RHEL 10), which theMaxwell M10GPUs cannot use. Keep Secure Boot off for this VM.

See Nvidia for more references here (background on module streams: NVIDIA blog)off.

Reboot the VMVM:

sudo reboot

Step 5: Verify the installation

After the VM reboots, open a terminal and run the nvidia-smi. command.

nvidia-smi

If the installationinstall wasworked, successful,it thisshows command will display information about your NVIDIAthe GPU, confirmingwhich confirms that it is being used by the RHEL guest and that passthrough is workingusing correctly.it Youthrough should see something like this:passthrough:

$ nvidia-smi

Wed Oct 15 20:13:14 2025       
+-----------------------------------------------------------------------------------------+

| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |

+-----------------------------------------+------------------------+----------------------+

| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |

| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |

|                                         |                        |               MIG M. |

|=========================================+========================+======================|

|   0  Tesla M10                      Off |   00000000:03:XX:00.0 Off |                  N/A |

| N/A   36C    P8              8W /   53W |       3MiB /   8192MiB |      0%      Default |

|                                         |                        |                  N/A |

+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+

| Processes:                                                                              |

|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |

|        ID   ID                                                               Usage      |

|=========================================================================================|

|  No running processes found                                                             |

+-----------------------------------------------------------------------------------------+

Step 6: SetupSet forup Containercontainers

Add NVIDIA's container toolkit repository (NVIDIA's documented repo; the CUDA reporepository above also carries the same packages):

curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | \
  sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo

Install the NVidiaNVIDIA containerContainer toolkit packages:Toolkit:

sudo dnf install -y nvidia-container-toolkit

Use only this repo. The oldolder nvidia-docker2 / nvidia-docker and nvidia-container-runtime reposrepositories and packages are not part of NVIDIA's current install guide.guide; use the repository above.

Docker

Once the NVidia container toolkit is installed, configureConfigure Docker to use the NVIDIA runtime and restart it:

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

EnsureTest that everything is setup correctly for the docker system:it:

sudo$ docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

You should see something like this

Podman (CDI)

For Podman, RHEL's built-in engine):container use CDI

For Podmanengine, NVIDIA recommends CDI instead of nvidia-ctk runtime configure. Since toolkit v1.18 the nvidia-cdi-refresh service generates the CDI spec is generated automatically at /var/run/cdi/nvidia.yaml by the nvidia-cdi-refresh service.. It runs when the toolkit or driver is installed or upgraded and at boot. List the devices:

nvidia-ctk cdi list

Regenerate the spec if needed (for example after removing a driver):

sudo systemctl restart nvidia-cdi-refresh.service
# or manually
sudo nvidia-ctk cdi generate --output=/var/run/cdi/nvidia.yaml

Test:Test it:

podman run --rm --security-opt=label=disable --device=nvidia.com/gpu=all ubuntu nvidia-smi

NoteTroubleshooting: Worthy:driver missing after a kernel update

With kernel-devel-matched installed, DKMS rebuilds the module automatically after kernel updates. If nvidia-smi stops working after a kernel update, check:

dkms status

This showscheck which kernels the module is built for.for:

dkms status

Example fromoutput thiswhere VMthe (builtmodule is only built for an older el9_4 kernel):kernel:

nvidia/580.95.05, 5.14.0-427.42.1.el9_4.x86_64, x86_64: installed

Make sure kernel-devel for the running kernel is installed, then build itthe module for the current kernelkernel. (useUse the version shown by dkms status):

sudo dkms install nvidia/580.95.05

Container

References

NVidia Docs:
• NVIDIA Container Toolkit install guide, and CDI support

• Ollama Docker imageConfiguration
  (container configuration)
• NVIDIA blog: driver module streams on RHEL (background)

Sources (checked 7 OctOctober 2026)

• Red Hat: RHEL release dates (RHEL 9.8 GA 2026-05-19)
• NVIDIA Driver Installation Guide: RHEL, Recent Updates (proprietary modules removed), Advanced Options (nvidia-boot-update)
• NVIDIA supported drivers and NVIDIA blog: R580 last branch for Maxwell/Pascal/Volta
• NVIDIA Container Toolkit install guide, CDI support, sample workload
• Broadcom KB 334594 (EFI + 64-bit MMIO sizing), KB 312208 (DirectPath I/O requirements), KB 309986 (Toggle Passthrough), KB 313456 (open-vm-tools)

Older instructions (before Oct 2026) - do not use on the M10 VM

Kept for reference only. latest-dkms now installs a driver branch that does not support the Tesla M10.

sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
sudo dnf module install nvidia-driver:latest-dkms