Guide: Proxmox + Older NVIDIA GPUs (GTX 1060 / P4000 / V100) LXC Passthrough Guide

itamarbudin · 2026-05-25T15:43:08+0200

After spending way too much time fighting NVIDIA driver issues, DKMS errors, and Proxmox kernel issues, I finally got a clean, reliable setup working for older NVIDIA GPUs with LXC passthrough.

This setup works well for:

GTX 1060 / 1070 / 1080
Quadro P4000 / P5000
Tesla V100
Most Pascal / Volta cards

Use cases:

Ollama
CUDA
Docker
Plex transcoding
Local LLMs
AI inference
GPU acceleration inside LXC containers

IMPORTANT NOTES

You do NOT need:

NVIDIA vGPU
enterprise licensing
pve-nvidia-vgpu-helper
patched kernels
weird passthrough scripts

Also:

Avoid Linux Kernel 7.x right now with older Pascal/Volta GPUs
Many users are hitting DKMS compile failures on kernel 7
Kernel 6.x is MUCH more stable right now

Recommended:

Proxmox kernel 6.8 or 6.14
NVIDIA 550 or 575 drivers

HOST INSTALLATION

Install required packages:

Code:

apt update
apt install -y build-essential dkms pve-headers-$(uname -r)

Download NVIDIA driver (550 stable branch):

Code:

wget https://us.download.nvidia.com/XFree86/Linux-x86_64/550.163.01/NVIDIA-Linux-x86_64-550.163.01.run

Alternative newer driver (575 branch):

Code:

wget https://us.download.nvidia.com/tesla/575.57.08/NVIDIA-Linux-x86_64-575.57.08.run

Make executable:

Code:

chmod +x NVIDIA-Linux-x86_64-550.163.01.run

OR for 575:

Code:

chmod +x NVIDIA-Linux-x86_64-575.57.08.run

Install driver:

Code:

./NVIDIA-Linux-x86_64-550.163.01.run

OR:

Code:

./NVIDIA-Linux-x86_64-575.57.08.run

Installer recommendations:

YES to DKMS
NO to OpenGL libraries if headless

Reboot:

Code:

reboot

Verify:

Code:

nvidia-smi

You should now see the GPU on the Proxmox host.

LXC GPU PASSTHROUGH

Edit your container config:

Code:

nano /etc/pve/lxc/.conf

Basic passthrough config:

Code:

lxc.cgroup2.devices.allow: c 195:* rwm
lxc.cgroup2.devices.allow: c 509:* rwm
lxc.cgroup2.devices.allow: c 511:* rwm

lxc.mount.entry: /dev/nvidia0 dev/nvidia0 none bind,optional,create=file
lxc.mount.entry: /dev/nvidiactl dev/nvidiactl none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-modeset dev/nvidia-modeset none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm dev/nvidia-uvm none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm-tools dev/nvidia-uvm-tools none bind,optional,create=file

Restart container:

Code:

pct restart

MULTIPLE GPU SETUPS

If your server has multiple GPUs, things work a little differently.

Example:

GPU0 = GTX 1060
GPU1 = Tesla V100
GPU2 = Quadro P4000

Each GPU gets its own device node:

Code:

/dev/nvidia0
/dev/nvidia1
/dev/nvidia2

You can verify GPU numbering with:

Code:

nvidia-smi -L

or:

Code:

ls -l /dev/nvidia*

Passing Through ONLY One Specific GPU

Example: pass ONLY GPU1 (Tesla V100) into container.

Use:

Code:

lxc.mount.entry: /dev/nvidia1 dev/nvidia1 none bind,optional,create=file
lxc.mount.entry: /dev/nvidiactl dev/nvidiactl none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-modeset dev/nvidia-modeset none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm dev/nvidia-uvm none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm-tools dev/nvidia-uvm-tools none bind,optional,create=file

This isolates the container to ONLY that GPU.

Passing Through Multiple GPUs

Example: expose GPU0 and GPU2 to the same container:

Code:

lxc.mount.entry: /dev/nvidia0 dev/nvidia0 none bind,optional,create=file
lxc.mount.entry: /dev/nvidia2 dev/nvidia2 none bind,optional,create=file

Keep the shared NVIDIA devices:

Code:

lxc.mount.entry: /dev/nvidiactl dev/nvidiactl none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-modeset dev/nvidia-modeset none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm dev/nvidia-uvm none bind,optional,create=file
lxc.mount.entry: /dev/nvidia-uvm-tools dev/nvidia-uvm-tools none bind,optional,create=file

Restricting CUDA to Specific GPUs

Inside the container, you can limit visible GPUs using:

Code:

export CUDA_VISIBLE_DEVICES=0

or multiple GPUs:

Code:

export CUDA_VISIBLE_DEVICES=0,1

VERY useful for:

Ollama
Docker
AI workloads
Multi-user servers

Docker + NVIDIA Toolkit Inside LXC

If running Docker inside the container:

Code:

apt install -y nvidia-container-toolkit

Test with:

Code:

docker run --rm --gpus all nvidia/cuda:12.3.2-base-ubuntu22.04 nvidia-smi

INSIDE THE CONTAINER

Download the same NVIDIA driver version used on the host.

550 branch:

Code:

wget https://us.download.nvidia.com/XFree86/Linux-x86_64/550.163.01/NVIDIA-Linux-x86_64-550.163.01.run

575 branch:

Code:

wget https://us.download.nvidia.com/tesla/575.57.08/NVIDIA-Linux-x86_64-575.57.08.run

Install userspace libraries ONLY:

550 example:

Code:

chmod +x NVIDIA-Linux-x86_64-550.163.01.run

./NVIDIA-Linux-x86_64-550.163.01.run --no-kernel-module

575 example:

Code:

chmod +x NVIDIA-Linux-x86_64-575.57.08.run

./NVIDIA-Linux-x86_64-575.57.08.run --no-kernel-module

Test GPU access:

Code:

nvidia-smi

If everything is correct, the container should now see the GPU.

OPTIONAL: CUDA INSTALLATION INSIDE LXC

Download CUDA installer matching the NVIDIA driver version.

CUDA 12.9.1 + NVIDIA 575.57.08:

Code:

wget https://developer.download.nvidia.com/compute/cuda/12.9.1/local_installers/cuda_12.9.1_575.57.08_linux.run

Make executable:

Code:

chmod +x cuda_12.9.1_575.57.08_linux.run

Run installer:

Code:

./cuda_12.9.1_575.57.08_linux.run

IMPORTANT:

Skip driver installation during CUDA setup
The NVIDIA driver is already installed on the Proxmox host
Inside LXC you only need CUDA userspace libraries/tools

Recommended CUDA components:

CUDA Toolkit
CUDA Runtime
nvcc
CUDA libraries

You can usually skip:

Driver
DKMS
Kernel modules

VERIFY CUDA

Check GPU visibility:

Code:

nvidia-smi

Check CUDA compiler:

Code:

nvcc --version

You should now have:

CUDA working inside LXC
GPU passthrough working
Docker/Ollama/AI workloads accelerated

EXAMPLE NVIDIA-SMI OUTPUT

Example nvidia-smi output after successful passthrough:

Code:

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 575.57.08 Driver Version: 575.57.08 CUDA Version: 12.9 |
|-----------------------------------------+------------------------+----------------------|
| 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 V100-SXM2-16GB Off | 00000000:65:00.0 Off | 0 |
| N/A 36C P0 45W / 300W| 0MiB / 16384MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 1 Quadro P4000 Off | 00000000:B3:00.0 Off | N/A |
| 34% 42C P8 12W / 105W| 256MiB / 8192MiB | 3% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 2 GeForce GTX 1060 6GB Off | 00000000:C4:00.0 Off | N/A |
| 27% 39C P8 8W / 120W| 128MiB / 6144MiB | 1% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 1 N/A N/A 2481 C ollama 220MiB |
| 2 N/A N/A 3154 C python 110MiB |
+-----------------------------------------------------------------------------------------+

Useful commands:

Code:

nvidia-smi

Live monitoring:

Code:

watch -n 1 nvidia-smi

Show GPU list only:

Code:

nvidia-smi -L

Check CUDA compiler:

Code:

nvcc --version

OPTIONAL: ADD CUDA TO PATH

Add to ~/.bashrc:

Code:

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

Reload shell:

Code:

source ~/.bashrc

FINAL THOUGHTS

This setup ended up being WAY simpler than all the vGPU guides floating around online.

For older NVIDIA cards, standard LXC passthrough works perfectly fine for:

Ollama
CUDA
Docker
AI workloads
Plex

without needing enterprise NVIDIA features.

Hopefully this saves someone else a few hours of troubleshooting.

Impact · 2026-05-25T23:07:29+0200

Didn't you post this already? I remember answering to this with some suggestions.

itamarbudin · 2026-05-25T23:09:04+0200

Impact said:
Didn't you post this already? I remember answering to this with some suggestions.

I don't think so. I made some edits

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Guide: Proxmox + Older NVIDIA GPUs (GTX 1060 / P4000 / V100) LXC Passthrough Guide

itamarbudin

New Member

IMPORTANT NOTES

HOST INSTALLATION

LXC GPU PASSTHROUGH

MULTIPLE GPU SETUPS

Passing Through ONLY One Specific GPU

Passing Through Multiple GPUs

Restricting CUDA to Specific GPUs

Docker + NVIDIA Toolkit Inside LXC

INSIDE THE CONTAINER

OPTIONAL: CUDA INSTALLATION INSIDE LXC

VERIFY CUDA

EXAMPLE NVIDIA-SMI OUTPUT

OPTIONAL: ADD CUDA TO PATH

FINAL THOUGHTS

Impact

Distinguished Member

itamarbudin

New Member

We value your privacy

Guide: Proxmox + Older NVIDIA GPUs (GTX 1060 / P4000 / V100) LXC Passthrough Guide

itamarbudin

New Member

IMPORTANT NOTES​

HOST INSTALLATION​

LXC GPU PASSTHROUGH​

MULTIPLE GPU SETUPS​

Passing Through ONLY One Specific GPU​

Passing Through Multiple GPUs​

Restricting CUDA to Specific GPUs​

Docker + NVIDIA Toolkit Inside LXC​

INSIDE THE CONTAINER​

OPTIONAL: CUDA INSTALLATION INSIDE LXC​

VERIFY CUDA​

EXAMPLE NVIDIA-SMI OUTPUT​

OPTIONAL: ADD CUDA TO PATH​

FINAL THOUGHTS​

Impact

Distinguished Member

itamarbudin

New Member

We value your privacy

IMPORTANT NOTES

HOST INSTALLATION

LXC GPU PASSTHROUGH

MULTIPLE GPU SETUPS

Passing Through ONLY One Specific GPU

Passing Through Multiple GPUs

Restricting CUDA to Specific GPUs

Docker + NVIDIA Toolkit Inside LXC

INSIDE THE CONTAINER

OPTIONAL: CUDA INSTALLATION INSIDE LXC

VERIFY CUDA

EXAMPLE NVIDIA-SMI OUTPUT

OPTIONAL: ADD CUDA TO PATH

FINAL THOUGHTS